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Localization by learning of wave-signal distributions

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US9440354B2
US9440354B2 US14589429 US201514589429A US9440354B2 US 9440354 B2 US9440354 B2 US 9440354B2 US 14589429 US14589429 US 14589429 US 201514589429 A US201514589429 A US 201514589429A US 9440354 B2 US9440354 B2 US 9440354B2
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Prior art keywords
sensor
signal
device
pose
mobile
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US20150197011A1 (en )
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Steffen Gutmann
Ethan Eade
Philip Fong
Mario Munich
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iRobot Corp
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iRobot Corp
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    • BPERFORMING OPERATIONS; TRANSPORTING
    • B25HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25JMANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00Programme-controlled manipulators
    • B25J9/16Programme controls
    • B25J9/1656Programme controls characterised by programming, planning systems for manipulators
    • B25J9/1664Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B25HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25JMANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00Programme-controlled manipulators
    • B25J9/16Programme controls
    • B25J9/1694Programme controls characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02Control of position or course in two dimensions
    • G05D1/021Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G05D1/0234Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using optical markers or beacons
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02Control of position or course in two dimensions
    • G05D1/021Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0259Control of position or course in two dimensions specially adapted to land vehicles using magnetic or electromagnetic means
    • G05D1/0261Control of position or course in two dimensions specially adapted to land vehicles using magnetic or electromagnetic means using magnetic plots
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02Control of position or course in two dimensions
    • G05D1/021Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0268Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
    • G05D1/027Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means comprising intertial navigation means, e.g. azimuth detector
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02Control of position or course in two dimensions
    • G05D1/021Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0268Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
    • G05D1/0272Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means comprising means for registering the travel distance, e.g. revolutions of wheels
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02Control of position or course in two dimensions
    • G05D1/021Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0268Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
    • G05D1/0274Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means using mapping information stored in a memory device
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02Control of position or course in two dimensions
    • G05D1/021Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0276Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
    • G05D1/028Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using a RF signal
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D2201/00Application
    • G05D2201/02Control of position of land vehicles
    • G05D2201/0203Cleaning or polishing vehicle
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D2201/00Application
    • G05D2201/02Control of position of land vehicles
    • G05D2201/0206Vehicle in a health care environment, e.g. for distribution of food or medicins in a hospital or for helping handicapped persons
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D2201/00Application
    • G05D2201/02Control of position of land vehicles
    • G05D2201/0208Lawn mower
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D2201/00Application
    • G05D2201/02Control of position of land vehicles
    • G05D2201/0211Vehicle in an office environment, e.g. for delivering mail or for videoconferencing
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D2201/00Application
    • G05D2201/02Control of position of land vehicles
    • G05D2201/0215Vacuum cleaner
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10STECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S901/00Robots
    • Y10S901/01Mobile robot
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10STECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S901/00Robots
    • Y10S901/02Arm motion controller
    • Y10S901/09Closed loop, sensor feedback controls arm movement
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10STECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S901/00Robots
    • Y10S901/46Sensing device

Abstract

A robot having a signal sensor configured to measure a signal, a motion sensor configured to measure a relative change in pose, a local correlation component configured to correlate the signal with the position and/or orientation of the robot in a local region including the robot's current position, and a localization component configured to apply a filter to estimate the position and optionally the orientation of the robot based at least on a location reported by the motion sensor, a signal detected by the signal sensor, and the signal predicted by the local correlation component. The local correlation component and/or the localization component may take into account rotational variability of the signal sensor and other parameters related to time and pose dependent variability in how the signal and motion sensor perform. Each estimated pose may be used to formulate new or updated navigational or operational instructions for the robot.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S. application Ser. No. 12/940,937, filed Nov. 5, 2010, which claims the benefit under 35 U.S.C. §119(e) of U.S. Provisional Application No. 61/280,677, filed Nov. 6, 2009, the entirety of which is hereby incorporated by reference.

BACKGROUND

1. Field of Endeavor

What is disclosed herein relates to determining the position and orientation of an object.

2. Description of the Related Art

It is often necessary or useful to be able to determine the pose (location and orientation) of an object, person, pet, asset or device. Certain systems for determining the pose of an object equipped with a signal sensor are known. However, many mobile devices lack the resources necessary to run known systems themselves or are unable to implement them while performing additional tasks, and many such systems fail to effectively take into account practical issues such as manufacturing and production variances, variability of the surfaces of the area in which a mobile device operates (such as uneven terrain or floors), and complications resulting from signal interaction with environmental features such as walls or trees.

SUMMARY

Certain embodiments discussed in this application may be utilized in conjunction with systems and methods disclosed in U.S. Pat. No. 7,720,554, filed on Mar. 25, 2005, the content of which is hereby incorporated herein in its entirety by reference.

A method for accurately estimating the pose of a mobile object in an environment, without an a priori map of that environment, is disclosed. The method generates the estimates in near real-time, compensates for the rotational variability of a signal sensor, compensates for signal multipath effects, and is statistically more accurate than relying on dead reckoning or signal detection alone. The method comprises decomposing an environment to be navigated by the mobile object into two or more cells, each of which is defined by three or more nodes. An expected measure of a background signal is determined for each of the nodes, and an expected measure of the signal at positions interior to the cell is estimated based on the expected measure at each of two or more of the cell's nodes. The actual or expected measures at the nodes need not be known a priori, because as the mobile object navigates the environment, the mobile object maps the signal measure at substantially the same time as it localizes by using, for example, an appropriate implementation of an appropriate SLAM algorithm. During an initialization process, initial values for some or all of the calibration parameters including but not limited to the rotational variability, sensor error, and the like, are optionally determined. Also obtained is a scale parameter that correlates a position or location to an expected signal measure. The initialization process makes use of data from the signal sensor as well as a motion sensor and allows for initial determination of an expected signal measure at each of the nodes of a cell. During the SLAM phase, the pose of the mobile object is estimated based on some or all of the following: data from the motion sensor, data from the signal sensor, a map of expected signal measures, the calibration parameters, and previous values for these items. If the mobile object leaves a cell defined by initialized nodes, then the initialization process may be rerun to initialize any previously uninitialized nodes of the cell the mobile object enters. Optionally, some or all of the uninitialized nodes of the cell the mobile object enters are initialized by extrapolating from nodes of cells neighboring the entered cell.

Also disclosed is a method for accurately estimating the pose of a mobile object in an environment, without an a priori map of that environment, which estimates the pose in near real-time, compensates for signal multipath effects, and is statistically more accurate than relying on dead reckoning or signal detection alone. The method comprises decomposing an environment to be navigated by the mobile object into two or more cells, each of which is defined by three or more nodes. An expected measure of a background signal is determined for each of the nodes, and the expected measure of the signal at positions proximate to those nodes is estimated based on the expected measure at each of two or more of the cell's nodes. The actual or expected measures at the nodes need not be known a priori, because as the mobile object navigates the environment, the mobile object maps the signal measure at substantially the same time as it localizes by using, for example, an appropriate implementation of an appropriate SLAM algorithm.

During an initialization process, initial values for some or all of the calibration parameters, including but not limited to rotational variability, sensor error, and the like, are optionally determined. Also obtained is a scale parameter that correlates a position to an expected signal measure. The scale parameter may become less accurate for positions (locations) not proximate to the cell or the nodes of the cell. The initialization process makes use of data from the signal sensor as well as a motion sensor and allows for initial determination of the expected signal measure at each of the nodes of a cell.

During the SLAM phase, the pose of the mobile object is estimated based on some or all of the following: data from the motion sensor, data from the signal sensor, the map of expected signal measures, the calibration parameters, and previous values for these items. If the mobile object moves or is moved so that is not proximate to the nodes or to an earlier position, then the initialization process may be rerun to initialize any previously uninitialized nodes of the cell the mobile object enters. Optionally, some or all of the uninitialized nodes proximate to the mobile object's new position are initialized by extrapolating from previously estimated values associated with positions proximate to the uninitialized nodes.

“Proximate” is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (i.e., it is not to be limited to a special or customized meaning) and includes, without limitation, being less than 0.25 meters, 0.5 meters, 1 meter, 5 mobile device lengths, or less than 10 mobile device lengths apart. In some embodiments, proximate may be defined relative to the size of an environment if a measure of that size is obtained (e.g., 10% or 5% of environment width). In some embodiments, proximate may be defined relative to the mobile device (e.g., the distance traveled by the mobile device in 0.5 seconds or 1 second). Poses may be proximate if their locations are proximate. Orientations may also be proximate. For example, two poses may be proximate if they differ by less than a particular amount, such as but not limited to 1, 2, 5, or 10 degrees. In some embodiments, two poses are proximate if both their locations and orientations are proximate. In other embodiments, only the locations of the poses are considered. A pose may be proximate to a location or position.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed aspects will hereinafter be described in conjunction with the appended drawings, which are provided to illustrate and not to limit the disclosed aspects. Like designations denote like elements.

FIG. 1 illustrates an example embodiment of a mobile device configured to learn signal distributions for use in localizing and navigating an environment.

FIG. 2 is a functional logical diagram illustrating example functional elements of an embodiment of such a mobile device.

FIG. 3 illustrates an example physical architecture of an embodiment of such a mobile device.

FIG. 4 illustrates a linear relationship between the actual (“truth”) ground position of a mobile device and the output of a sensor detecting signals at that ground position.

FIG. 5 illustrates a non-linear relationship between the actual (“truth”) ground position of a mobile device and the output of a sensor detecting signals at that ground position.

FIG. 6 is a flow chart of an example localization filter initialization process.

FIG. 7 illustrates an example embodiment of a signal sensor for localization.

FIG. 8 is a cross-section of the sensor of FIG. 7.

FIG. 9 illustrates a top-down perspective of an illustrative example operating environment with a grid of sensor measurement points.

FIG. 10 illustrates an example of rotational variance of signal measurement as well as detected variation in the signal throughout the environment of FIG. 9.

FIG. 11 illustrates bilinear interpolation used by some embodiments.

FIG. 12 is a flow chart illustrating an example use of GraphSLAM for localization.

FIG. 13 illustrates an example 8-neighborhood of a node.

FIG. 14 illustrates an example extrapolation of localization values for a new node from a neighboring pair of nodes.

FIG. 15 is a flow chart illustrating an example use of EKF SLAM for localization.

FIGS. 16-22 illustrate an example development of an information matrix in an embodiment using EKF SLAM for localization.

FIG. 23 is a flow chart illustrating an example use of ESEIF-SLAM for localization.

FIG. 24 illustrates example results of using odometry (dead-reckoning) alone to follow a navigational plan.

FIG. 25 illustrates example results of using an example embodiment of background signal localization to follow a navigational plan.

FIGS. 26 and 27 illustrate example signal strength maps generated by an embodiment.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Described herein are methods and systems for the localization of an object, such as a mobile object (e.g., a robotic floor cleaner). Certain embodiments may utilize such mobile object localization to navigate the mobile object. By way of illustration and not limitation, the mobile object may optionally be an autonomous, semiautonomous, or remotely directed floor cleaner (e.g., a sweeper, a vacuum, and/or a mopper), delivery vehicle (e.g., that delivers mail in a building, food in a hospital or dormitory, etc.), or monitoring vehicle (e.g., pollution or contaminant detector, security monitor), equipped with one or more drive motors which drive one or more wheels, tracks, or other such device, where the drive motors may be under control of a computing device executing a program stored in non-transitory memory (e.g., it persists when the object is powered down or when some other data is overwritten or erased).

Example embodiments will now be described with reference to certain figures. Through the description herein, “localization” may include determining both the position of an object in an environment and the orientation of that object. The combination of position and orientation is referred to as the “pose”. Either or both of the position (or location) and orientation may be absolute (in terms of a logical reference angle and origin) or relative (to another object).

Many objects, including mobile objects, are not functionally or physically symmetrical. Knowing the orientation of such objects may be useful in determining how to navigate such objects in an environment. For example, some mobile objects can only move forward and some mobile objects may have functional components, such as vacuum ports or sweepers, at specific locations on their surface. Also, the current orientation of a mobile object may affect its future position as much as its current position does if it moves in the direction of its orientation. Thus, determining the pose of a mobile object may be of great assistance in determining how to navigate the mobile object to perform a task, such as a floor cleaning task, in an efficient manner.

For convenience, much of this disclosure is expressed in terms of localizing a “mobile device”. However, the disclosed aspects may generally be used to localize types of objects, and one of skill in the art will understand how the disclosure can be applied to objects that are not independently mobile (such as those that are transported or carried by something else) and to objects that are not devices (e.g., pets equipped with collars or humans carrying appropriately configured tags or computing devices).

Typically, when performing tasks such as vacuum cleaning, lawn mowing, delivery, elderly care, etc., an autonomous or mobile device needs to know its pose with respect to its environment in order to reach its goal or accomplish its task in an effective way. For example, toys and other devices might be intended and configured to behave in a particular manner when they are in a particular location. Even if the device itself has no additional task or goal that benefits from localization, if its pose can be determined then the location of a person or other entity carrying or otherwise attached to the device can be determined. If the relative orientations of the carrier and the device are known, then the pose of the carrier can be determined.

The methods and systems disclosed herein advance the state of the art in how the pose of an autonomous device is computed from a combination of observations of a vector field that varies over space and measurements from motion sensors such as odometers, gyroscopes, accelerometers, internal measurement units (IMU) or other dead-reckoning devices (generically referred to as “dead-reckoning sensors” and the output of which is generically referred to as “odometry” or “motion measurements”). Measurements (e.g., measurements of change in position or orientation) from a motion sensor may be relative to another position or may be absolute. Such measurements may include measures of location or distance (e.g., distance or direction of travel) as well as measures of object orientation (e.g., amount of rotation from a previous orientation or amount of rotation from an absolute reference). Wave or other signals emitted into an environment by an external source can create an appropriate vector field. Example methods and systems disclosed herein use a localization and mapping technique, such as a simultaneous (which may be substantially simultaneous) localization and mapping (SLAM) framework, for estimating object pose, parameters modeling rotational variability, and parameters describing the signal distribution or vector field in the environment.

Example embodiments incorporating certain disclosed aspects can localize and track a mobile device with higher accuracy than conventional methods that ignore complications such as rotational variability or multi-path effects. Some embodiments do so in a way that requires no a priori map of the environment or of the signal strength in that environment. Some disclosed embodiments can optionally do so while using relatively inexpensive amounts of computational resources such as processing power, storage, and time, such that the functionality disclosed herein can be made available in a relatively compact mobile device and/or it can be distributed in affordable mass market consumer goods, including products which perform additional functionality beyond localizing, mapping, or navigating. Pose estimates can be obtained in near real time in some such embodiments and some embodiments run in constant or substantially constant time, with storage requirements linear or near linear based on the size of the environment for a given node size (i.e., for a given node size, it is linear in the number of nodes).

FIG. 1 illustrates an example context or environment in which an object 100 such as a mobile device may be situated. The environment 110 in this example includes left wall 120, right wall 130, front wall 135, ceiling 140, and floor or ground 150. One or more signal sources 180 generate background wave signals—the aforementioned vector field. The mobile device 100 includes a signal detector 170 configured to detect the signals generated by the sources 180 and a dead-reckoning (motion) sensor 190 to report on observed motion.

U.S. Pat. No. 7,720,554 discloses, among other things, a low-cost optical sensing system for indoor localization. A beacon 160 projects a pair of unique infrared patterns or spots 180 on the ceiling 140. The beacon 160 can be placed relatively freely in the environment 110 and adjusted such that it points towards the ceiling 140. An optical signal sensor 170 measures the direction to both spots 180 on the ceiling 140. The signal sensor 170 then reports the coordinates of both direction vectors projected onto the sensor plane. These beacon spots 180 are the signal sources in an example embodiment that is used throughout this disclosure. Other embodiments may use more or fewer spots 180. Other wave signals such as those used in Wi-Fi, GPS, cellular networks, magnetic fields, sound waves, radio-frequency identification (RFID), or light can also be used. Corresponding sources include wireless routers, satellites, cell towers, coils, speakers, RFID transmitters, and projectors. For example, appropriately configured ceiling lights or speakers may be used in certain embodiments. Although the illustrated embodiment uses a dedicated projector 160 to generate the signal sources 180, in other embodiments pre-existing or off-the-shelf generators can be used. For example, in an apartment complex or a yard, a detector 170 may be configured to take advantage of the distinct Wi-Fi signals available from the various Wi-Fi routers that may be within range. Similarly, existing lights, including fixed ceiling lights, may be used with photo-sensitive sensors. Other signal sources may generate soundwaves (audible, subsonic, or ultrasonic) and the detector 170 may be configured to detect the generated waves. Thus, no or minimal modification to the environment is necessary for such embodiments to be effective. Digital signals, including those transmitted by radio and/or as used in wireless communications may also be used.

Because an indoor embodiment is used to illustrate many of the disclosed aspects, those aspects are disclosed in the context of an indoor environment. However, the disclosed aspects are not limited in this way and can operate outdoors as well as indoors.

A system that tracks the pose of a mobile device 100 equipped with a signal sensor 170 by relying, even in part, on the values reported by that sensor 170 faces a number of challenges. Typically, the signals sensed by the sensor 170 will have a different strength or value at different locations in the environment. In the illustrated scenario, the mobile device 100 moves along the ground 150 (although one of skill could readily apply what is disclosed to a mobile device that travels along a wall or ceiling, or that moves (and rotates) in three dimensions). One challenge is relating a change in the detected (sensed) signal to a change in ground position. The relationship between sensed signal and ground position is the “scale” parameter.

Another challenge stems from the construction, manufacture, or assembly of the sensor 170, performance properties of the second 170, and/or its association with or coupling to the mobile device 100. In some embodiments the orientation of the sensor 170 is fixed relative to the environment 110 and is independent of the rotation of the mobile device 100. For example, a gyroscopic or inertial system may be used to rotatably attach the sensor 170 to the mobile device 100 such that when the mobile device turns or rotates, the sensor rotates in a counter direction. In other embodiments the sensor 170 is rigidly affixed to or integrated with the mobile device 100 such that its orientation is substantially fixed relative to the orientation of the mobile device 100. Indeed, in this disclosure the position and orientation of the sensor 170 are presumed to be identical to that of the mobile device 100 so that, for example, “sensor 170” is used interchangeably with “device 100” when discussing pose or motion. As discussed below, this assumption simplifies the disclosure. One of reasonable skill can readily account for any fixed or calculable offset between the orientation of the sensor 170 and the device 100.

Ideally, rotation of the sensor 170 relative to the environment 110 should not affect the detected signal or should affect it in a way that depends only on the degree of rotation. For example, the direction to signal sources 180 changes when rotating the sensor 170, but the magnitude of the signal at that position is not changed. However, some sensors have directional sensitivities. For example, a Wi-Fi receiver can show changes in signal strength when the antenna is rotating as a result of the device on which it is mounted (e.g., the mobile device) rotating. Even in such a situation, the variation might be predictable and calculable. However, errors in manufacturing, misalignments in attaching the sensor on the object, uneven flooring, and the like may introduce an additional, difficult to predict, variation in the orientation of the signal sensor 170 relative to the orientation of the device 100. This may lead to seemingly unpredictable variation in the signal strength detected by the sensor 170. Thus, for example, a sensor 170 measuring bearing and elevation relative to sources 180 can show variations due to calibration errors of the sensor's vertical axis. This parameter is referred to herein as “rotational variability”.

A third challenge in determining the pose of a mobile device arises from the multiple paths from the signal sources 180 to the sensor 170. In general, a sensor 170 may receive a wave signal not only directly from a source 180 but also through reflections on walls 120, 130, 135 and other stationary and non-stationary objects in the environment (e.g., furniture, trees, and humans). The direct path as well as each reflection may contribute to the signal measured on the sensor 170. This can create non-linear and seemingly arbitrary distributions of the signal throughout the environment 110. This effect is referred to herein “multi-path”.

Some embodiments of the methods and systems disclosed are configured to operate when some or all of the following conditions are met:

First, a given signal can be uniquely identified relative to other signals so that when a signal is detected at different times in an environment 110 with multiple signals, a correspondence between the signals can be maintained. For example, signals in Wi-Fi, GPS and other networks contain a unique ID as part of their data packet protocol. Active beacons, such as those disclosed in U.S. Pat. No. 7,720,554, may encode a signature (e.g., by modulating the signal, such as by modulating a light that forms light spots on a ceiling).

Second, signals are substantially continuous and change over space but optionally not in time. It should be understood that continuity does not mean that there is necessarily a one-to-one correspondence of vector of signal values to ground positions. The same measurement vector might be observed at several different locations in the environment 110 because, for example, of multi-path. Some embodiments may operate with signals that change in time, where the change over time is known or can be predicted.

Third, a dependency on orientation can by described by signal sensor orientation and rotational variability. In other words, knowing the signal values at one pose (position and orientation) enables expected signal values for other orientations at the same position to be calculated if the change in sensor orientation and any rotational variability are known.

FIG. 2 illustrates an example functional block diagram of an embodiment of a localization system. A dead reckoning sensor 190 provides relative motion data (odometry). Information from the dead reckoning sensor may be used to estimate, in whole or in part, the device's current position based upon a previously determined position and advancing that position using a known or estimated speed over an elapsed period of time.

The dead reckoning (motion) sensor 190 may include multiple instances of multiple types of dead reckoning sensors such as those mentioned above. A signal sensor 170 provides measurement vectors of the signals in the environment. The signal sensor 170 may include multiple instances of one or more types of sensing components. In some embodiments the signal sensor 170 may include one or more sensors which detect more than one different types of signals (e.g., the signal sensor 170 may include both Wi-Fi sensors and light sensors). Some such embodiments may use only one signal type at a time; some such embodiments may normalize the output of the signal sensor and proceed as if there were only one type of (composite) signal being sensed; and some embodiments may extend what is disclosed below in obvious ways by using the availability of more signal sensor data to improve the filtering results.

The output of sensors 170, 190 are provided to a Vector Field SLAM module 220. The illustrated SLAM module 220 reads and stores information 230 about a grid of nodes. The SLAM module 220 also provides pose estimates of the mobile device 100 and map information about the signal distribution in the environment 110. These may be provided to other components for use and/or display. For example, pose estimates may be provided to a navigational component 240, which directs the mobile device 100 to move to a new location based at least in part on its current pose. They may also be provided to an alerting or action system 250 which uses the current pose as at least a partial basis for subsequent action such as cleaning. The map may be stored for future use and/or displayed for diagnostic purposes, for example.

Even though many appropriate signal sources may be present or could be made available, and although appropriate signal sensors may be configured on an embodiment, some embodiments will optionally not use GPS, not use WiFi, not use direct light signals (e.g., non-reflected light from lamps or infrared sources), and/or not make use of ceiling lighting fixtures for some or all aspects of the localization process.

FIG. 3 illustrates example physical components of an appropriately configured example device 100. The dead reckoning sensors 190 and signal sensors 170 are instantiated by components such as those described above. Those physical sensors may include their own processors and/or local storage components and may be configured to normalize data and generate standardized output signals. The sensor components may communicate with one or more processors 310. The processor may be, for example, a specially configured chip or a more general processor executing software. Regardless, it is configured in accordance with what is disclosed herein. The processor may include its own storage, but it may be advantageous for the device 100 to include additional memory or storage 320 to store any necessary software and the data necessary to implement the methods disclosed below. In some embodiments the sensors may also store data directly in the memory 320. Software for implementing aspects of what is disclosed would typically be stored in ROM, flash memory, or some other form of persistent storage, although volatile storage may be used as well. Data may be stored in volatile (e.g., can be erased when the system powers down) and/or non-volatile memory (which stores the data for later access even if the device is powered down and then powered up again). The processor 310 and storage 320 may also be used for functional purposes not directly related to localization. For example, the mobile device 100 may use them when performing navigation or when performing tasks such as cleaning or guarding. In other embodiments, the processing and storage capacity are dedicated to localization and mapping and the device contains additional computational capacity for other tasks.

The processor 310 may be operatively connected to various output mechanisms such as screens or displays, light and sound systems, and data output devices (e.g., busses, ports, and wireless or wired network connections). The processor may be configured to perform navigational routines which take into account the results of the SLAM process. Executing a navigational process may result in signals being sent to various controllers such as motors (including drive motors or servomotors), brakes, actuators, etc, which may cause the mobile device 100 to move to a new pose (or to perform another activity, such as a cleaning function). The move to this new pose may, in turn, trigger additional output from the sensors to the processor, causing the cycle to continue. An example embodiment is configured with an ARM7 processor, 256K of flash ROM for software, and 64K of RAM for data. These are not minimum requirements—some or all of what is disclosed herein can be accomplished with less processing and storage capacity. Other embodiments may be different processors and different memory configurations, with larger or smaller amounts of memory.

Turning back to FIG. 1, the signal sensor 170 measures bearing and elevation to two or more of the projected spots 180 on the ceiling 140. Bearing and elevation can be translated into (x, y) coordinates in a sensor coordinate system by projecting them onto the sensor plane, which in the illustrated example embodiment is typically less than 10 cm above the ground 150 and is substantially parallel to it. In addition to the signal coordinates, the amount of light from each spot 180 is measured as the signal magnitude.

The geometry of the illustrated localization system results in a linear model for position estimation in an ideal environment without multi-path signals. That is, if the sensor 170 moves one meter in one direction, the sensor coordinates change by a certain amount (depending on the scale parameter, which is proportional to the height of the ceiling 140). If the sensor 170 then moves another meter into the same direction, the sensed signals change by the same amount. FIG. 4 illustrates this property by using measurements of a sensor 170 mounted on a fixed path (or “rail”) along which the sensor 170 moves in a fixed and known direction. The rail is an experimental platform for evaluating the systems and methods described herein which allows the ground position of the sensor 170 to be known to observers and which also allows the orientation of the sensor 170 to be controlled. On the x-axis the position on the rail is shown. The y-axis shows the y coordinate of one of the spots 180 in sensor units.

In situations such as that shown in FIG. 4, the linear distribution of the wave signal can be used directly for the localization of the sensor 170 in conjunction with other system parameters. For example, in the embodiment illustrated in FIG. 1 with two spots 180, these parameters could be chosen as per Equation (1), where s1 and s2 are scale factors for each spot 180 and m0=(m0,x1 m0,y1 m0,x2 m0,y2)T contains absolute offsets (m0,x1 m0,y1)T for the first spot 181 and (m0,x2 m0,y2)T for the second spot 182.
v init=(s 1 ,s 2 ,m 0)  (1)

From these parameters, an expected signal value h=(hx1, hy1, hx2, hy2)T at a sensor position (x y)T can be calculated as:

( h x 1 h y 1 h x 2 h y 2 ) = ( m 0 , x 1 m 0 , y 1 m 0 , x 2 m 0 , y 2 ) + ( s 1 0 0 s 1 s 2 0 0 s 2 ) ( x y ) ( 2 )

It is straightforward to extend this model for an arbitrary number of spots 180.

For general wave signals, a similar linear model can be chosen. In general, the following model in Equation (3) applies, where h is the vector of estimated signal values for position (x y)T, h0 is the absolute offset in the sensor space, and A0 is a general scale matrix.

h = h 0 + A 0 ( x y ) ( 3 )

A flow chart for computing the parameters of this linear model (either Equation 2 or Equation 3) is shown in FIG. 5. At state 510, sensor measurements are obtained from the signal sensor 170. When a sensor measurement is obtained, data about the concurrent pose of the device 100 is also obtained (e.g., at the same or substantially the same time), such as from one or more on-board dead-reckoning sensors 190 or from separate monitoring systems. State 510 continues while the device 100 travels a short distance. At state 520, a RANSAC method (or, more generally, any algorithm for fitting data into a linear model) is run. At state 525 the status of the process is evaluated. Based on, for example, the number of data points evaluates (which may be set to 2, 5, 10, or more), the amount of time elapsed (which may be set to 1 second, 5 seconds, 10 seconds, 30 seconds, or more), or the quality of the data fitting algorithm (which may be set to be about or above a particular threshold), an embodiment may determine the initialization is sufficient. If so, then at state 530, the output of RANSAC is used to initialize the parameters for the relevant equation. If not, the initialization process continues.

RANSAC (Random Sample Consensus) is an iterative method to estimate the parameters of a mathematical function from sensor data that include outliers (see, e.g., A. Fischler, R. C. Bolles. Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Comm. of the ACM, Vol 24, pp 381-395, 1981). The RANSAC algorithm runs several iterations. In a given iteration a number of measurements are chosen at random (the term “random” as used herein, encompasses pseudo random). In an embodiment using two spots 180, two signal sensor 170 readings each containing measurements to both spots 180 are sufficient. In an example implementation, it was determined that additional sample readings per iteration did not produce a significant improvement on the results and increased the resources consumed by the RANSAC process. From the chosen measurements the parameter values are determined by solving the set of equations arising from placing the chosen measurements into the mathematical model, Equation (2). More generally, Equation (3) may be used. The computed parameters are then evaluated using some or all available sensor data, optionally including dead reckoning data. This usually computes a score such as the number of inliers or the overall residual error. After completing the desired number of iterations, the parameter values with a score meeting certain criteria (e.g., the best score) are chosen as the final parameters.

Embodiments may use variations of RANSAC or alternatives to it.

Illustrative examples of the parameters used during initialization are presented below, in the discussion of GraphSLAM.

Once the initialization process is complete or the parameters for the relevant equation are otherwise determined, one or more algorithms for accounting for noisy sensors and dead-reckoning drift can be used to implement a system to effectively track the pose of the mobile device 100 with more accuracy, in less time, and/or with lower computational resource requirements than many conventional methods. Examples of such algorithms include the Kalman Filter, the Extended Kalman Filter (EKF), the Invariant Extended Kalman Filter (IEKF), and the Unscented Kalman Filter (UKF). However, the ability of these filters to effectively track pose after the initialization process of FIG. 500 tends to degrade in environments where the distribution of the wave signal is non-linear. But even in environments, such as room 110, where the wave signal is distorted (e.g., by multi-path), the linear model described here is still useful for the initialization of non-linear systems according to what is disclosed herein.

As discussed above, multi-path occurs when the wave signal not only reaches the signal sensor 170 directly but also in other ways, such as by reflecting from nearby objects or walls (e.g. the right wall 130 in FIG. 1). As the sensor 170 moves closer to wall 130, due to occlusion and limited field of view, the sensor 170 receives more signal contributions from wall reflections. The result is a shift in the signal back to a position that appears to be further away from the wall 130.

FIG. 6 illustrates this scenario where right wall 130 reflects the signal from the spots 180. Note how the curve 610 bends over and switches to the opposite direction: when the mobile device 100 is 3 meters from its starting point the sensor 170 is reporting a detected value of approximately −0.3, the same value it reported at approximately 1.5 meters, instead of the expected value of approximately −0.55 predicted by a linear model. This compression of the sensor signal appears with any wave signal that shows reflections from walls or other objects. It makes position estimation particularly difficult because a range of signal sensor readings do not match to exactly one ground position but instead have a least two ground position candidates. Even more candidates are possible when taking measurements in 2D or higher dimensions, or when the multipath pattern involves multiple objects, for example.

However, if the expected signal strength as a particular location is known, then signal strength measurements can still be used for localization in a multi-path environment via, for example, a Bayesian localization framework such as an EKF. In an example embodiment, by way of illustration, a piece-wise linear approximation (pieces are illustrated in FIG. 6 by the solid vertical lines 620) is used to substantially simultaneously learn the signal shape or “map” (the strength of the signal throughout the environment) and estimate the pose of the mobile device 100. This is done using a simultaneous localization and mapping (SLAM) approach.

The second challenge mentioned was rotational variability. When turning a sensor 170 in place, the measurements of the observed vector signal can change. This is the rotational variability of the sensor 170. For example, a sensor 170 in an embodiment using spots 180 outputs (x y) coordinates of the center of a spot 180 on the sensor plane. The (x y) coordinates essentially are a vector representing bearing and elevation to the spot 180. Ideally, as the sensor 170 rotates in place, only the bearing should change—the elevation should stay constant. In practice, however, elevation changes (usually, but not always, by a relatively small amount) due to variations in manufacturing, calibration errors, or misalignments in mounting the sensor 170 on the mobile device 100.

For example, FIG. 7 shows a top-down perspective of an example of one embodiment of a signal sensor 170 mounted on a mobile device 100. Although FIG. 1 represents the sensor 170 as protruding from the mobile device 100, FIG. 7 depicts an embodiment in which the sensor 170 is recessed in a cavity or depression with a substantially circular perimeter (although other perimeters could also be used). The sensor 170 comprises four infrared photodiodes 710 mounted on a pyramidal structure 720. The top of the pyramid 720 does not contain a photodiode 710 and is substantially coplanar with the top surface of the mobile device 100. In other embodiments, the sensor 170 may have a different structure including, for example, more or fewer photodiodes 710 arranged in a similar or different configuration. The approach described herein can be adapted to account for the geometric properties of the sensor 170 used. In the arrangement shown in FIG. 7, each of the photodiodes 710 measures incoming light by producing an electric current substantially proportional to the received light. Each of the two opposing photodiode pairs is then used for measuring the direction of light on the corresponding axis. Below, the computation of the light direction and the effects of rotational variability for the x axis of the sensor are discussed. The computations for the y axis are analogous. Thus, what follows describes a mathematical system that models rotational variability for the signal sensor 170 of FIG. 7 and can be readily adapted to a wide range of signal sensors.

FIG. 8 illustrates a representation 800 of the sensor 170 of FIG. 7, simplified for the purposes of clarity. Only the pair of photodiodes 710 measuring along the x axis is shown. Light from one of the spots 180 (it can be assumed to be spot 181 without any loss of generality) is directed at the sensor 170 as illustrated by light vectors 810. The x coordinate reported by the sensor 170 is proportional to the tangent of the elevation angle (β) to spot 181. This tangent of β is measured through the two currents i1 and i2 of the opposing photodiodes 801 and 802, respectively. The angle α of the pyramid is a parameter that may vary among embodiments. Some embodiments may have an adjustable angle α. The below assumes that α is greater than zero or that such an effect is simulated (e.g., through the use of apertures above the photodiodes which cast shadows and limit the exposure of the photodiodes to light from the spots.). Generally, the larger the angle α is, the larger the sensitivity of the sensor 170 to changes in location, but the smaller the field of view (e.g., the closer the sensor 170 must remain to the spots). While any effective angle α between 0 and 90 degrees may be used, it is preferably within the range of 15 to 75 degrees. Some embodiments may use, for example, 30, 45, or 60 degrees.

The coordinate hx1 of spot 181 is equal to the tangent of β and is measured by:

h x 1 = i 1 - i 2 i 1 + i 2 = tan β tan α . ( 4 )

The rotational variability is modeled by an offset in β that changes with the orientation of the sensor 170 such that Equation (5) holds, where β′ is the angle to the ideal axis of rotation perpendicular to the ground plane and βε is the angular error that changes with rotation.
β=β′+βε  (5)

Inserting (5) in (4) and applying the rule of the tangent of the sum of angles yields:

i 1 - i 2 i 1 + i 2 = tan ( β + β ɛ ) tan α = tan β + tan β ɛ 1 - tan β tan β ɛ tan α ( 6 )

Since βε is small, tan βε is approximated by:

tan β ɛ = sin β ɛ cos β ɛ β ɛ 1 = β ɛ ( 7 )

Substituting (7) into (6) yields:

i 1 - i 2 i 1 + i 2 tan β + β ɛ 1 - β ɛ tan β tan α ( 8 )

For elevation angles β′ that are much less then 90°, 1−βε tan β′ is approximated as 1, yielding Equation (9), where cx is the rotational variance on the x axis depending on the orientation of the signal sensor 170.

i 1 - i 2 i 1 + i 2 tan β tan α + β ɛ tan α = tan β tan α + c x ( 9 )

For the y axis of the sensor 170 another bias term cy is derived in an analogous way. Together both parameters form the vector c of rotational variability.

c = ( c x c y ) . ( 10 )

Since the direction β to the spots 180 can be arbitrary, the parameters for rotational variability are substantially independent of where the spots 180 are located. All spots 180 may therefore share substantially the same parameters.

Similar and analogous results can be obtained for other signal sources and sensor types. Rotational variability is not limited to the illustrated embodiment. Other sensor(s) 170 that measures bearing-to-signal sources 180 can show similar effects when the vertical axis of the sensor 170 is slightly misaligned or the sensor 170 otherwise rotates around an axis different from the ideal one. For example, antennas for radio or other wireless communication can show slight changes in the received signal when they rotate. Thus, an optional useful model of the way the vector of signal values changes on rotation of the sensor 170 is a function that only depends on the orientation of signal sensor 170 and parameters describing the rotational variability of the signal sensor 170.

FIGS. 9 and 10 illustrate rotational variability and non-linearity arising from multi-path signals. The two figures depict the environment of room 110 from a top down perspective. FIG. 9 shows a regular grid 900 consisting of 8×7 positions (every 50 cm in this example) on the floor 150. A system using spots 180 was deployed with an appropriately configured signal sensor 170. At a given location 910, sensor measurements were taken with eight different sensor orientations (every 45°).

The measurements were then rotated back and drawn in a common reference frame. FIG. 10 shows the resulting signal measurements using different symbols for the eight orientations. At a given location 910, the measurements form a ring which shows the rotational variability at this location. In this experiment the radius is almost constant over the entire room 110. The mean of rotational variability is about 0.0072 sensor units, which corresponds to an angular error of about βε=0.72°. The error caused by rotational variability can be constant (as in this example) but might also change over time or location, e.g., if the angular error βε is more significant or if there are other similarly variable sources of error, such as uneven floors or motion dependent device vibration, not modeled in Equations (4)-(9).

Changes in the pitch or angle of the mobile device relative to the surface it is traversing can also cause or contribute to rotational variability. For example, uneven floors or ground such as might result from rolling terrain, general bumpiness, twigs or branches, brickwork, and the like can cause the pitch of the mobile device to change. In some embodiments, rotational variability due to change in pitch is monotonic, although it complements rotational variability due to manufacturing and other sources At least some rotational variability due to changes in pitch may be accounted for using the methods described herein. For example, changes in pitch of less than 3, 5, or 7 degrees (or other pitches) may be accommodated by some embodiments without modification to what is disclosed herein.

FIG. 9 also shows the effect of multi-path signals. In the illustrated scenario, the walls on the left 120, right 130, and front 135 cause signal reflections. While the left wall 120 and right wall 130 create some level of signal compression, the front wall 135 causes severe reflections that make the signal bend over. Even worse, in the corners of the room, the signal is reflected from two walls and therefore the resulting measurement is even more distorted.

Although there is significant signal distortion, it has been determined that the error is systematic and continuous. This allows modeling the nature of the signal using non-linear systems. An example embodiment approximates the non-linearity caused by multi-path through the use of piece-wise linear functions. This example technique is described below in greater detail. Other approximations, e.g., using Splines (piecewise polynomial (parametric) curves which may be used to approximate complex shapes using curve fitting) or Nurbs (non-uniform rational basis splines, which are mathematical models which may be used to generate and represent surfaces and curves) may also be used and may provide more accurate representations of the non-linear signal distortion. However, experimentation with certain embodiments has indicated that the use of bi-linear interpolation results in faster processes and produces sufficiently good results in embodiments that have limited computational resources. Embodiments with more computational resources or those with relaxed time constraints may beneficially use other representations, including Splines or Nurbs.

In some embodiments, localization of a mobile device 100 equipped with a signal sensor 170 is performed by learning the signal distribution in the environment 110 while at the same time (or at substantially the same time) localizing the mobile device 100. This is known as simultaneous localization and mapping (SLAM). As discussed above, in the following it is assumed that the pose of the mobile device 100 and the signal sensor 170 are substantially identical. In some embodiments they are not, and it is straightforward to add, for example, a fixed coordinate transformation between the two poses. However, assuming pose identity facilitates understanding of the various disclosed aspects.

In SLAM, a device moves through a time series of poses x0 . . . xT, xt=(x, y, θ)εSE(2), in an environment (e.g. room 110) containing N map features m1 . . . mN, miε

M. Here SE(2) is the space of poses in the 2 dimensional plane and M the space of the map features. Without loss of generality, x0=(0, 0, 0)T. At each time step t=1 . . . T the system receives a motion input ut (e.g., odometry from dead reckoning sensors 190) with covariance Rt and a measurement zt (e.g., of signal strength from signal sensors 170) with covariance Qt.

The motion input ut is measured, for example, by motion sensors 190 on the mobile device 100 and describes the change in pose of the sensor 170 from time step t−1 to t. As mentioned above, in certain embodiments the motion input may be provided by external sensors or a combination of internal and external sensors. The input vector ut is associated with a covariance Rt that models the accuracy of the pose change. Typical motion sensors 190 include wheel encoders, gyroscopes, accelerometers, IMUs and other dead-reckoning systems. A motion model defined by a function g describes the motion of the device 100 since the previous time step where eu is a zero mean error with covariance Rt:
x t =g(x t-1 ,u t)+e u  (11)

An example of input ut is a forward translation d followed by a rotation α: ut=(dα)T. Equation (11) then resolves into the following form:

x t = ( x y θ ) + ( d cos θ d sin θ α ) + e u ( 12 )

For those skilled in the art it is straightforward to substitute different motion models g and input vectors ut depending on the geometry of the mobile device 100 and available motion sensors 190. The systems and methods disclosed herein apply regardless of the motion model.

When the signal sensor 170 on the mobile device 100 obtains a new reading zt of the wave signals, the SLAM system uses a sensor model to predict the observation. As in the case of motion, the sensor reading zt is associated with a covariance Qt modeling the accuracy of the measurement. The sensor model is defined by a function h that predicts an observation given the sensor 170 pose at time step t and map features as in Equation (13), where ez is a zero mean error with covariance Qt. The sensor model h depends on the map features and the available signal sensor 170 in the mobile device 100. In early SLAM applications such as those described in Thrun et al. [2005, Chapter 10], map features are landmarks and the sensor model h computes bearing and distance to them. The systems and methods disclosed herein optionally use a very different approach: some or all of the features are signal values at predetermined or fixed locations and, few or none of the features are landmarks in the environment. The expected values of wave signals at a given device 100 pose are computed by h as follows.
z t =h(x t ,m 1 . . . m N)+e z  (13)

In SLAM it is possible to include in the sensor model calibration parameters like those describing rotational variability of the sensor 170. The SLAM algorithm then not only estimates device pose and map features, but also estimates the calibration parameters. All calibration parameters are summarized in a vector c. The size of this vector depends on the sensor 170. For example, in an embodiment using the reflection from spots of modulated light created by a project 160 as the signal sources 180, the calibration parameters include the two bias constants (cx, cy) in Equation (10). The observation model in Equation (13) then includes this parameter:
z t =h(x t ,c,m 1 . . . m N)+e z  (14)

Embodiments also learn the vector field generated by M signals over the environment. This vector field can mathematically be described as a function that maps a ground pose to a vector of M signal values.
VF:SE(2)→

M  (15)

Since signals are independent of sensor 170 orientation (per the preferences set forth above), the space of poses SE(2) can be decomposed into position and orientation. The vector field over position is then modeled as a piece-wise linear function by laying a regular grid of node positions bi=(bi;x; bi;y)T, i=1 . . . N onto the ground 150 (or onto whatever surface the mobile device 100 is traversing). This creates rectangular cells with one node at each of the cell's four corners. Each node i holds a vector miε

M describing the expected signal values when placing the sensor at bi and pointing at a pre-defined direction θ0. Returning to the running example of signal sources 180 being spots of modulated light, the vector mi holds four values—the coordinates of both spots 180: mi=(mi,x1, mi,y1, Mi,x2, Mi,y2)T.

The spacing of cells in the regular grid defines the granularity and precision with which the wave-signal distribution in the environment 110 is modeled. A finer spacing leads to more cells, yielding better precision but requiring more memory. A coarser spacing results in fewer cells, requiring less memory but at the possible cost of precision. The exact parameter for the cell size depends on the environment, mobile device, and the application. For the purpose of covering an environment 110 with reasonable precision (e.g., for systematic floor cleaning), the cell size could be 0.5 m to 2 meters for a system using spots of frequency modulated light as signal sources 180 in an environment with a ceiling height of 2.5 to 5 meters.

For an arbitrary sensor position with orientation θ0, the expected signal values are computed by bilinear interpolation from the nodes of a cell (e.g., the four nodes) containing the sensor position. Such a cell is illustrated in FIG. 11. The four nodes may be determined from the sensor position at time t and node positions bi. “Current cell” refers to the cell in which the sensor is positioned at the current time step t. Let xt=(x, y, θ) be the sensor pose and bi0 . . . bi3 the cell nodes enclosing the sensor 170 as shown in FIG. 11.

The expected signal values at (x, y) with orientation θ0 are then computed as Equation (16), where moo, mi0, mi1, mi2 and mi3 are the signal values at the four cell nodes and w0, w1, w2 and w3 are the weights of the bilinear interpolation computed as Equation (17).

h 0 ( x , y , m 1 m N ) = w 0 m i 0 + w 1 m i 1 + w 2 m i 2 + w 3 m i 3 ( 16 ) w 0 = ( b i 1 , x - x ) ( b i 2 , y - y ) ( b i 1 , x - b i 0 , x ) ( b i 2 , y - b i 0 , y ) w 1 = ( x - b i 0 , x ) ( b i 2 , y - y ) ( b i 1 , x - b i 0 , x ) ( b i 2 , y - b i 0 , y ) w 2 = ( b i 1 , x - x ) ( y - b i 0 , y ) ( b i 1 , x - b i 0 , x ) ( b i 2 , y - b i 0 , y ) w 3 = ( x - b i 0 , x ) ( y - b i 0 , y ) ( b i 1 , x - b i 0 , x ) ( b i 2 , y - b i 0 , y ) . ( 17 )

The final expected signal values are computed by taking into account sensor orientation θ and the parameters c describing the rotational variability of the sensor 170:
h(x t ,c,m 1 . . . m N)=h R(h 0(x,y,m 1 . . . m N),θ,c).  (18)

Here hR is a continuous function that transforms the interpolated signal values obtained through Eq. (16) by the sensor orientation and rotational variability. This is usually a rotation by orientation θ followed by a correction with the rotational variability c. In the running example, turning the sensor 170 in place causes the spot 181 coordinates to change according to the rotation angle θ but in the opposite direction. The rotational component hR therefore becomes Equation (19), where (hx1, hy1, hx2, hy2) is the output vector of Equation (16). It is also possible to formulate the equations for a variable number of spots 180 since the components in Equations (16)-(19) are not correlated between spots 180. Similar equations can be readily obtained for other signal sources.

h R ( h x 1 , h y 1 , h x 2 , h y 2 , θ , c x , c y ) = ( cos θ sin θ 0 0 - sin θ cos θ 0 0 0 0 cos θ sin θ 0 0 - sin θ cos θ ) ( h x 1 h y 1 h x 2 h y 2 ) + ( c x c y c x c y ) ( 19 )

It is possible to apply more complex schemes for predicting the sensor signal that use more than only the four nodes of the current cell. A cell with fewer nodes could also be used. In another embodiment, the function in Equation (16) is evaluated for the current and several neighboring cells and then a weighted mean of them is computed as the final result. The weights are taken as the mass of probability of the current position estimate that falls into each cell. The weight of a given cell is a function of the probability that the sensor or mobile device is within this cell. This probability can be derived from the current mobile device pose and associated uncertainty as it is computed by the localization filter.

The above understandings and equations enable the application of a SLAM algorithm for estimating device path, rotational variability, and/or the signal values at the node positions. Optionally, full SLAM and/or on-line SLAM may be used.

In full SLAM, the complete trajectory of the device 100, rotational variability of the sensor 170, and/or some or all map features are computed. For example, the state that is estimated is:

Y = ( x 1 x T c m 1 m N ) . ( 20 )

One algorithm that computes an estimate of Y is GraphSLAM, which is used in some embodiments and is described in more detail below.

In contrast, on-line SLAM estimates the current pose and some or all map features at each time step t=1 . . . T. The state estimated at each time step t is:

y t = ( x t c m 1 m N ) . ( 21 )

There are several algorithms that estimate yt over time. Examples using EKF-SLAM, EIF-SLAM and ESEIF-SLAM are described below. Embodiments may use any of the described full SLAM or on-line SLAM algorithms, as well as other algorithms. Some embodiments can be configured to use a particular SLAM algorithm depending on, for example, a user's preference, the computational resources available, and other operational constraints.

GraphSLAM is a non-linear optimization method for estimating the state vector in Equation 20 by finding the values in Y that best explain the sensor and motion data from sensors 170 and 190. GraphSLAM estimates Y as the solution to a non-linear least squares problem in finding the minimum of the following objective function where the quantities are defined as described before:

J = t = 1 T ( x t - g ( x t - 1 , u t ) ) T R t - 1 ( x t - g ( x t - 1 , u t ) ) + t = 1 T ( z t - h ( y t ) ) T Q t - 1 ( z t - h ( y t ) ) ( 22 )

An example implementation of GraphSLAM is illustrated in FIG. 12. One general approach is to first provide an initial estimate of the state vector Y at state 1210. This may be based on, for example, data from the dead reckoning sensors 190 or data from the signal sensors 170. Then the embodiment approximates motion model g(·) and sensor model h(·) by linear models using Taylor expansion at the current estimate of the state vector at state 1220. This results in a quadratic function of Equation (22). The linear equation system that reduces or minimizes the quadratic function obtained in state 1220 is solved or optimized at state 1230. This provides an improved estimate of Y. The second and third states are repeated until the solution converges to a desired degree at state 1240. If sufficient convergence is not obtained, then optimization state 1230 is repeated. If it is obtained, then at state 1250 a path is output.

The linear equation system may optionally be solved during optimization state 1230 using Conjugate Gradient, since the system is usually sparse and positive definite.

For providing an initial estimate of the state vector in state 1210, the following method can be used. First, the initial device poses x1 . . . xT are computed from x0=(0, 0, 0)T by iteratively applying the motion model in (11) for each t=1 . . . T. Second, the initial rotational variability is c=ĉ where ĉ is a rough guess about the values of rotational variability that depend on the sensor 170. In the running example, some embodiments use ĉ=(0, 0)T because the rotational variability is usually small. The initial node values mi are computed from Equations (1) and (2). For example, the parameters in Equation (1) are computed by applying RANSAC over a short initial sequence, as discussed above. The node values mi are then obtained from the node position bi through Equation (2).

The short initial sequence typically contains a minimum or relatively low number of sensor samples (e.g., 2 to 50) while the mobile device 100 moves a certain distance. This distance is usually proportional to the chosen cell size such that enough samples are available that cover a reasonable fraction of the cell. For example, for a cell size of 1 meter, the distance threshold may be selected within the range of 0.5 m to 1 meter. More generally, some embodiments may be configured to travel a distance of ⅓ to ⅔ of the cell size. This distance may also depend on the size of the mobile device 100: typically, larger mobile devices should travel further during the initialization phase. Optionally, a given sample is spaced a minimum distance from an adjacent sample. This distance may be determined based on a dynamically configured initialization travel distance and sample count, for example. It may also be fixed a priori so that samples are taken after every half second of travel or after every 10 centimeters of travel, for example, although other time periods and distances may be used.

GraphSLAM may be implemented as a batch method since the motion and sensor data needs to be available when computing the non-linear optimization. Furthermore, the amount of computation is significant. These constraints may make it difficult to use GraphSLAM in certain embedded systems with limited computational resources, such as if the mobile device 100 is a conventional vacuum cleaner or other consumer product. GraphSLAM is nevertheless useful as a baseline algorithm for computing the best possible result given the sensor data and a chosen model. For example, it can be used during the development of products or selectively run when computational resources are available to check the performance of other methods. Further, there are certain embodiments of product mobile devices where there are sufficient computational and memory resources to utilize GraphSLAM.

One such method for state estimation used by some embodiments is an Extended Kalman Filter (EKF). The EKF is a non-linear variant of the Kalman Filter (KF). EKF-SLAM is an on-line SLAM method. The state vector contains the current pose of the device 100 but not older or future poses (or estimates thereof). Furthermore, the size of the state grows as the mobile device 100 moves in the environment 110. Initially the state contains only device pose, rotational variability and the node estimates of the 4 nodes of the initial cell.

y 0 = ( x 0 c m 1 m 2 m 3 m 4 ) ( 23 )

As the mobile device 100 moves around and visits further cells, the system grows by augmenting the state vector with further nodes. After t time steps and visiting cells with a total of n nodes the state becomes:

y t = ( x t c m 1 m n ) ( 24 )

The EKF computes an estimate of this state by maintaining mean and covariance modeling a Gaussian distribution over the state.
y˜N(μ,Σ)  (25)

The initial mean is set to equation (26), where ĉ is a rough guess/estimate of the rotational variability of the sensor 170 and {circumflex over (m)}1 . . . {circumflex over (m)}4 are initial values of the four nodes obtained from sensor data of a short initial sequence as described before using Equations (1) and (2). Again, in a sample embodiment using spots 180, the initial rotational variability can be set to ĉ=(0, 0)T.

μ 0 = ( x 0 c ^ m ^ 1 m ^ 2 m ^ 3 m ^ 4 ) ( 26 )

The initial covariance is a diagonal matrix where the vehicle uncertainty is set to 0 and the uncertainties of rotational variability and the four initial nodes are infinite. For implementation on a computer, ∞ can be replaced by a large number.

0 = ( 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ) ( 27 )

On object motion ut with covariance Rt, EKF-SLAM updates the state as Equations (28) and (29), where f extends the motion model g over all state variables and Fy is its Jacobian with respect to state per Equations (30)-(31).

μ _ t = f ( μ t - 1 , u t ) ( 28 ) _ t = F y t = 1 F y T + R t ( 29 ) f ( y t - 1 , u t ) = ( g ( x t - 1 , u t ) c m 1 m N ) ( 30 ) F y = f y ( μ t - 1 , u t ) ( 31 )

When a new sensor observation zt with covariance Qt is taken, the system determines the current cell, i.e. the cell in which the mean estimate of current device pose {circumflex over (x)}t falls, and then updates the mean and covariance of the state.

In general the current cell at time t can be:

1. A cell where all four nodes are already part of the state vector.

2. A cell where at least one node but not all four nodes are part of the state vector.

3. A cell where none of the four nodes are in the state vector.

In the first case no changes are required to the state vector and the system can continue updating mean and covariance as described further below.

In the second and third cases, nodes not yet present in the state vector need to be added by augmenting the state with the new nodes. In general, adding a node to the state vector containing n nodes is achieved by Equations (32) and (33), where {circumflex over (m)}n+1 and Mn+1 are the mean and covariance of the new node. This mean and covariance can be computed from nodes already contained in the state vector by linear extrapolation per Equations (34) and (35), where Ai, i=1 . . . n are matrices weighting the contribution of each node in the extrapolation, M is the covariance over all nodes, and S is additional noise for inflating the new covariance to allow the new node to vary for accommodating the non-linear structure of the wave signal. In some embodiments and in certain scenarios, the vector field changes slowly over space (i.e., the signal is relatively constant). Thus, in such embodiments, change between adjacent nodes is limited and extrapolation might degenerate into a linear model. Some embodiments use a smaller S in introduced in such circumstances, and some embodiments use introduced a larger S if the vector field is known or predicted to change more rapidly over space.

μ _ t ( μ _ t m ^ n + 1 ) ( 32 ) _ t ( _ t 0 0 M n + 1 ) ( 33 ) m ^ n + 1 = i = 1 n A i m ^ i ( 34 ) M n = ( A 1 A n ) M ( A 1 A n ) + S ( 35 )

The initialization of a new node is graphically illustrated in FIGS. 13 and 14. In an embodiment, a new node 1330 is initialized by taking into account the 8-neighborhood directions around the new node 1330, as illustrated in FIG. 13. As shown in FIG. 14, for each of the eight directions, the two neighbors on the straight line from the new node 1330 are used to extrapolate the mean and covariance of the new node. For any such pair the new node can be computed as shown in FIG. 14. The mean and covariance are computed from node j1 1340 and j2 1350 only. Both nodes contain the mean estimates of both sensor spots. The corresponding contribution matrices are:

A j 1 = - 1 2 ( 1 0 1 0 0 1 0 1 1 0 1 0 0 1 0 1 ) A j 2 = - 1 2 ( 3 0 1 0 0 3 0 1 1 0 3 0 0 1 0 3 ) ( 36 )

The extrapolation is such that the mid point between the spots 180 is used for extrapolation. The orientation of the line between the two new spot estimates is taken over from the closer one. This has the effect that changes in orientation are not propagated when initializing new nodes.

Some embodiments optionally only consider cases where a new node can be initialized from a pair of the 8 directions. In case there are several possible candidates, an embodiment may chose the one with the smallest resulting covariance Mn. For comparing covariances, the matrix determinant, the trace of the matrix, its Frobenius norm, or other norms can be used.

If there are no neighbors for initialization, some embodiments discard the sensor observation. Such a situation may occur, for example, when the mobile device 100 travels over a full cell without any sensor 170 observations and then arrives in a cell where all four cells are not yet part of the state vector (scenario 3, above). In this scenario, the utility of the new observation for localization may be minimal. Nonetheless, some embodiments may still initialize a new node by linear combinations of other nodes in the state vector using Equations (34) and (35). Some embodiments may optionally only use the motion updates (e.g., the odometry from the dead reckoning sensors 190) of the mobile device 100 and wait until the device 100 returns to an existing cell or to a cell that can be initialized. Another approach is to start over and re-initialize the system from the current pose.

Once the state vector contains elements for all nodes of the current cell, the mean and covariance are updated with the measurement zt and its covariance Qt by application of the EKF equations per Equations (37)-(40) where h(yt) is the sensor model defined in Eq. (18), Hy the Jacobian of the sensor model and K the Kalman gain.

μ t = μ _ t + K ( z t - h ( μ _ t ) ) ( 37 ) t = ( I - KH y ) _ t ( 38 ) H y = y h ( μ _ t ) ( 39 ) K = _ t H y T ( H y _ t H y T + Q t ) - 1 ( 40 )

A flow chart of the EKF-SLAM method for object localization is shown in FIG. 15. At state 1510, the initial parameters are set per (26) and (27). At the next time interval, if there is a motion update such as from the dead reckoning sensors 190 then it is applied at state 1530 per (28) and (29). If there is a value from the signal sensor 170, and if a new cell is needed, it is initialized at state 1540 per (32)-(36). After it is initialized, or if no new cell was needed, then a sensor update is performed at state 1550 per (37) and (38). After any necessary updates, a new pose is output at state 1560 and the process continues with the next time period.

In general, EKF-SLAM has the advantage that it is an on-line method, integrating motion/odometry and signal sensor measurements as they appear. The most computationally expensive operation is the update of the covariance matrix on sensor update in Eq. (38), state 1550. This involves the update of large numbers (e.g., all) of the matrix elements, an operation that takes time quadratic in the number of nodes in the state.

In general, the covariance Σt is fully correlated. That is, there are few, if any, elements that are zero. This typically requires holding the full matrix in a data memory, which may limit the applicability of the method for embedded systems or other environments if there are overly limited memory resources.

An additional step in the EKF as well as in other filters is outlier rejection. In the case where measurements are received that seem implausible, the filter rejects these measurements. This may be accomplished by not updating the filter on such measurements, which may be the result of hardware errors, signal interference, or irregular timing problems, for example.

There are several options for detecting such outliers. For example, the sensor measurement itself can be examined for valid data. By way of illustration, a threshold on the absolute magnitude of the signal strength reported by a sensor if the range of allowable magnitudes for the signal being detected is known. If the measurement falls below or above this threshold it is rejected.

Another way to detect outliers is by comparing the received measurement zt with the expected one h(μ t). If the difference (e.g., as reported by means of the Mahanalobis distance, which is based on correlations between variables via which different patterns can be identified and analyzed) is too large, the measurement is rejected.

Another approach used by some embodiments for state estimation is an Extended Information Filter (EIF). The EIF is similar to the Extended Kalman Filter in that it models a Gaussian distribution over the state space and processes motion and signal sensor data on-line. Its parameterization, often called a dual representation, differs from that used by EKF. The parameterization consists of an information vector ηt and an information matrix Λt that are related to the mean μt and covariance Σt of the EKF in the following way:
ηtt −1μt
Λtt −1  (41)

The EIF-SLAM algorithm processes data from the motion sensors 190 and signal sensors 170 in the same way as EKF-SLAM described above. The computation of information vector and information matrix on object motion and sensor measurement can be derived from Eqs. (26) to (40) by inserting Eq. (41) and simplifying the resulting equations.

In general a direct application of the EIF-SLAM algorithm does not provide a greater advantage than EKF-SLAM. Under some approximations, however, it is possible to keep the information matrix sparse, i.e. many elements are zero, allowing for a more compact storage and more efficient updates in terms of time and computational resources.

EIF-SLAM has the property that when inserting a signal sensor 170 measurement, only those elements in the state the measurement depends on need to be updated in the information matrix. For Vector Field SLAM this means that only elements related with the device 100's object pose and rotational variability and with the four nodes of the current cell are updated. All other elements in the information matrix stay unchanged. Therefore, the update on signal sensor 170 information turns only few elements from zero into non-zero and generally preserves the sparsity of the information matrix.

However, the update on device motion (e.g., when new data from the motion sensors 190 is received) causes a full update of the whole information matrix in the general case. This causes the information matrix to become non-zero in most if not all elements, which may destroy any sparsity that was present before the motion update.

Some embodiments may use strategies for approximating the update of the information matrix on device motion that preserve the sparsity of the information matrix. Two such methods are the Sparse Extended Information Filter (SEIF) and the Exactly Sparse Extended Information Filter (ESEIF).

Yet another approach available to some embodiments for state estimation is ESEIF. The principle of the ESEIF algorithm is maintaining a set of “active features”. In the original context, “features” refer to landmarks. In the case of Vector Field SLAM, the features are the nodes. The active features are a subset of all features. Typically those features that are currently observed by the mobile device 100 are the active ones. Other features are called “passive”.

Only the active features contain cross-information between the pose of the device 100 and the feature (where the cross-information between device pose and feature is non-zero for active features, whereas for passive features this cross-information is zero). A feature can change its state from passive to active at any time without the need of special operations. The cross-information between device pose and feature starts as zero and becomes non-zero when updating the system on device motion.

Changing an active feature to a passive one requires computationally non-trivial operations that approximate the actual information matrix by a sparsification. ESEIF-SLAM conceptually integrates out the device pose and then re-localizes the device 100 using observations from only those features (nodes) that should stay or become active. By integrating out the device pose, the state becomes free of the pose. Any uncertainty in the device pose is moved into the feature estimates through the cross-information between device pose and feature. When re-localizing the device 100, only the features used in the signal sensor 170 observation then establish non-zero cross information. This way the sparseness of the information matrix is preserved.

The following describes an implementation of the ESEIF algorithm in the context of Vector Field SLAM. FIGS. 16-22 show information matrices supporting this description. Initially the system starts with 4 nodes, as in Equation (23). The corresponding information matrix is shown in FIG. 16. Only the diagonal blocks in the information matrix contain information and are non-zero, as indicated by black solid squares. All other entries are zero (shown as white). The diagonal blocks refer to the device pose xt, the rotational variability c and the initial 4 nodes m1 . . . m4.

In an example embodiment, as long as the object stays within this initial cell, the system updates the complete information matrix using all 4 nodes as active features. Eventually the matrix becomes fully dense (most if not all elements become non-zero), as illustrated in FIG. 17.

When the mobile device 100 moves out of the current cell and enters a different cell, the procedure of integrating out the device pose, initializing new nodes, and re-localizing the device takes place. First, the uncertainty of the device pose is integrated out. This moves information from the object pose into the rotational variability and the 4 nodes through their cross information. The result is an information matrix as shown in FIG. 18, which usually contains stronger information between nodes than before and lacks a device pose.

Next, new nodes are initialized and added to the state. For example, two new nodes m5 and m6 may be added as shown in FIG. 19. This indicates that the device 100 moved into a neighboring cell sharing nodes m3 and m4 with the initial one. The processing necessary for the addition of these nodes is described below. Note that the description also applies for other situations where 1, 3, or 4 new nodes need to be added, or, in embodiments that use cells with greater than four nodes, more than four new nodes need to be added.

The initial values for the information vector and matrix are obtained similarly to Equations (32)-(36), but in the information form as set out in Equation (41). The new information matrix then becomes the one as shown in FIG. 19. Note that there is no cross information between the new nodes and other entries in the state.

The pose of the device 100 is then reintroduced. In the original ESEIF algorithm, an object is localized through observations of active features. In this application of Vector Field SLAM algorithm this is performed in two steps. First, the state is augmented with the new device pose as shown in FIG. 19.

The entries for the new device pose in information vector and matrix are computed using Equation (41) and the following mean and covariance per Equations (42) and (43), where R0 is a parameter that increases the uncertainty of the new device pose. Thus, the new device pose stays unchanged but becomes less certain. At this time there are no active nodes since all cross information between device pose and nodes are zero. Any four nodes can be chosen as the next active set of features. Since the device 100 is in the cell defined by nodes m3 . . . m6, those nodes are chosen as the next set of active features.
μtt-1  (42)
Σtt-1 +R 0  (43)

On signal sensor measurement zt, the uncertainty of the device pose is reduced and elements related to rotational variability and the four active nodes m3 . . . m6 are updated. This creates new cross-information between device pose, rotational variability, and active nodes as shown in FIG. 21. Note that there is no cross information between nodes m1, m2 and nodes m6, m6. This shows how the information matrix stays sparse.

As the device 100 moves within the current cell, in this example embodiment optionally only the device pose, rotational variability, and active cells m3 . . . m6 are updated, as was noted during the discussion of the initial situation. When the device 100 moves into another cell, the state is extended and the information vector and matrix are augmented with new nodes as described above. If the new cell has been visited before, no new nodes need to be added to the state. In either case, the same procedure of integrating out device pose followed by re-localization takes place.

FIG. 22 shows the information matrix after a longer run of the system configured as described. The state contains a total of 29 nodes. The device pose (x, y, θ)T consists of three variables, rotational variability (cx, cy)T consists of two variables, and each node (mi,x1, mi,y1, mi,x2, mi,y2)T consists of four variables. This leads to a total of 3+2+4*29=121 variables. Non-zero information is indicated by solid blocks whereas white areas are zero information. The device pose contains cross information to the currently active nodes only (around rows 80 and 110). On the other hand, rotational variability contains cross information to all nodes. The nodes themselves have cross-information to spatially neighboring cells, which are at most eight neighbors per node. Overall the matrix is significantly sparse. From the 121×121=14641 entries in the information matrix, only 3521 or approximately 24% are non-zero. Furthermore since the matrix is symmetric, only the upper or lower half needs to be stored. This allows for compact storage and efficient computation within the ESEIF-SLAM algorithm—an efficient use of computational resources.

The mathematical equations for motion update (e.g., from the dead reckoning motion sensors 190), signal sensor update (e.g., from the sensors 170), and sparsification can be formulated directly in the information space, i.e. only using η and Λ for storing the state between motion and sensor updates. In addition an estimate of the mean μ is needed for computing the Jacobians of motion and sensor model.

A flow chart of an example implementation of the ESEIF-SLAM algorithm for object localization is shown in FIG. 23. It is similar to the EKF-SLAM algorithm, with an initialization state 2300, a motion update state 2310 if there is new motion (odometry) data, a signal update state 2340 if there is new signal sensor data, preceded by a new-node initialization state 2320 if new nodes are added, but also with an additional sparsification state 2330 that integrates out device pose and re-localizes the device 100 when changing to another cell. Also, there is another state 2350 for recovering the current mean μt from the information space by solving an equation system. After the solving state 2350, a new device pose is produced at state 2360 and the process repeats. This flow chart, like those illustrating the other algorithms, is illustrative. One of ordinary skill will make use of available optimizations when implementing an algorithm, including these algorithms.

The state vector as defined in (20) and (21) only contains one field for rotational variability. This is under the assumption that rotational variability does not change with location and thus can be shared among all nodes. There are, however, situations where this is not the case, e.g. when the error βε in Equation (5) is significant and the approximations in Equations (7)-(9) introduce a larger error, or when the sensor 170 is tilted due to uneven floor. There are different ways to deal with changing rotational variability.

In one embodiment each node contains its own estimate of rotational variability. The state vector of full SLAM in Equation (20) containing the full object path changes into Equation (44), with similar changes for the state of on-line SLAM in Equation 21.

y = ( x 1 x T m 1 c 1 m N c N ) ( 44 )

The rotational variability is computed similar to the expected node values by using bilinear interpolation per Equation (45), where Ci0, Ci1, ci2 and ci3 are the rotational variability estimates at the four cell nodes according to FIG. 11 and w0, w1, w2 and w3 are the weights from Equation 17. Using the obtained value for c the predicted measurement is computed as before using Equation 18.
c=w 0 c i0 +w 1 c i1 +w 2 c i2 +w 3 c i3  (45)

Initial estimates of rotational variability are 0 with a co-variance of total uncertainty. When initializing new nodes, the same techniques as described for initial mean and covariance of the node signal values apply for rotational variability.

The cost of storing rotational variability with each node is an increase in the number of state variables and therefore higher memory and run-time consumption. This can limit the application of this solution when computational resources are constrained.

In another embodiment, only one instance of rotational variability is kept, as originally defined in Equations (20) and (21), but it is allowed to change when the mobile device 100 moves. For EKF-SLAM this means that in the motion model in Equations (28)-(30), a component Vt is added to the sub-matrix of the rotational variability in the state covariance. Vt is an additive co-variance matrix modeling how much rotational variability is allowed to change when moving. It is usually a diagonal matrix of constant values.

In another embodiment, Vt=0 as long as the device 100 stays within a cell and Vt is set to a diagonal matrix with constant non-zero values on the diagonal only when the device 100 changes between cells. This has the advantage that while the device 100 stays within a cell, rotational variability is assumed to be constant and is only allowed to change when moving into another cell. In some situations this may offer a better approximation at the cost of additional computation time, but requires no significant additional computational space.

In another embodiment, Vt is used to allow a change in rotational variability when moving between cells in the ESEIF-SLAM system. In the sparsification state, the rotational variability is integrated out and re-localized as the device pose is. This is done because adding Vt in the information space would otherwise fully populate the information matrix, destroying or reducing its sparseness. The states for sparsification with rotational variability included are analogous to the previously described method. An additional advantage of this approach is the removal of cross-information between rotational variability and passive nodes. This further reduces memory requirements and saves computations, at least partially counteracting the additional computation necessary to perform the calculations.

These methods and systems may also be used for detecting and estimating “drift” on, for example, carpet. When a mobile device 100 moves on a carpeted surface, the carpet exhibits a force onto the mobile device 100 tending to slide or shift the mobile device 100 in a certain direction. This effect is caused by the directional grain, material, or other properties of the carpet. Other surfaces, such as lawns or artificial turf, may also exhibit similar properties.

The amount of this drift can be estimated by the localization filter in different ways. In one embodiment, the filter state in Equation (24) is augmented by two additional variables driftx and drifty that represent the amount of carpet drift in the x and y direction of the global coordinate frame. The motion model in Equation (11) then takes into account these new parameters and the filter estimates their values at the same time it estimates the other state variables.

In another embodiment, the mobile device 100 may be configured to move a certain distance forward followed by the same distance backward. From the difference in the position output of the localization system at the beginning and end of this sequence, the amount of carpet drift can be estimated because the carpet drift may be proportional to this position difference. Typically, such a distance would be small enough that it can be traversed rapidly but large enough that an appreciable difference can be detected and the results not obfuscated by noise. Some embodiments may use distances in the range of 10 cm to 2 meters. Some embodiments may use smaller distances. Some embodiments may use larger distances.

The systems and methods described above were evaluated by moving an indoor localization sensor 170, configured to detect infrared patterns 180 projected from a beacon 160, along a rail. Ground truth information—the actual pose of the sensor 170—was directly available from position and orientation sensors on the rail motor. Every 50 cm, sensed signal strength and other measurements were recorded with the sensor 170 in 8 different directions (every 45°), and approximately 50 readings were taken for each of those directions. Once the sensor 170 reached the end of the rail, it was moved 50 cm parallel to the previous rail line and another round of measurements was taken. This was repeated until a total of eight parallel tracks were completed. The previously discussed FIG. 9 shows the experimental setup with the ground truth positions of measurements. There is a wall 135 close to the rail at the top location. There are also walls on the left 120 and right 130 of the experimental space, but those walls are further from the sensor 170 than the upper wall 135 (at least when the sensor 170 is traversing the final rail. These walls contribute to multi-path signals and cause a significant disturbance of the sensor signal.

The previously discussed FIG. 10 shows the position of the sensor 170 directly determined by a linear sensor model in this environment. The compression on the left, right and top end is significant: a system using this linear model would loose significant accuracy in pose estimation.

Using the recorded data, a path for a virtual mobile device 100 through the grid was generated. Starting in the lower left corner the object moves along the rows and changes between rows on the left and right side. This results in a theoretically straightforward motion: along a row, a 90° turn at the end of the row, a brief movement to reach the next row, and then another 90° turn before traversing that next row. In practice, when zero-mean Gaussian noise is added to the motion information (simulating real-world error after extended use of dead-reckoning sensors), the odometry path is obtained as shown in FIG. 24. After attempting to move up and down the rail grid approximately ten times, the error in orientation is up to 90°: the mobile device is actually moving vertically when its own reckoning system indicates it is moving horizontally.

The simulated relative pose data and the resulting odometry path are plausible examples of internal motion estimates. Mobile devices such as autonomous vacuum cleaners or other consumer products can show a similar degradation of pose estimation when using the integration of wheel encoder counts as the only method for pose estimation for example.

For testing the Vector Field SLAM system, one of the approximately 50 sensor measurements from the ground truth pose was randomly chosen when reaching a grid position. This measurement was then provided to the SLAM method for object localization. The cell size for Vector Field SLAM was set to 1×1 meters. FIG. 25 shows the resulting object path. Although the figures speak for themselves, the conclusion is that a mobile device 100 equipped with a localization and mapping system as disclosed herein, can following a navigational plan with a dramatically higher degree of accuracy than one relying on dead reckoning alone. This result was computed using an implementation of EKF-SLAM. Similar results were obtained using GraphSLAM and ESEIF-SLAM implementations.

In another series of experiments, the accuracy of the individual Vector Field SLAM implementations was compared to ground truth. In general, all three methods provide higher accuracy than other methods that only use linear sensor models. The GraphSLAM method usually provided slightly better accuracy than EKF-SLAM and ESEIF-SLAM. The latter two usually provided similar accuracy. The absolute position error was determined to depend on several factors such as ceiling height and the size of environments. In the test environment, the overall mean position error was about 6 cm. In general, the sources of error may vary depending on the signal sources 180 used. For example, ceiling height may not be a significant contributor to error if the background signal used is generated by magnetic coils suspended over the operating environment.

Vector Field SLAM also provides information about the learned sensor model or map—the signal strength through the environment. FIGS. 26 and 27 show the learned coordinates for a signal source, in this example an infrared pattern 801 (the plots for a second infrared pattern or spot 802 are similar and omitted). Error bars indicate the 2 sigma levels of the mean values at each node position. One can see how the sensor signal is bent towards the rear wall 135. This shape is accounted for by the piece-wise approximation of the sensor signal.

A typical embodiment will run asynchronously in that a new time step is considered to occur whenever new data is available from signal sensor 170. This may be as often as six or seven times a second. In some embodiments, new sensor data may be ignored if the embodiment is still integrating previously available data and generating new pose information. In some embodiments the localization processor may request data from the signal sensor 170 or otherwise indicate that it is available to process that data. Some embodiments may run synchronously, with new data provided at fixed and regular time intervals.

The systems and methods disclosed herein can be implemented in hardware, software, firmware, or a combination thereof. Software can include compute readable instructions stored in memory (e.g., non-transitory memory, such as solid state memory (e.g., ROM, EEPROM, FLASH, RAM), optical memory (e.g., a CD, DVD, Bluray disc, etc.), magnetic memory (e.g., a hard disc drive), etc., configured to implement the algorithms on a general purpose computer, special purpose processors, or combinations thereof.

While certain embodiments may be illustrated or discussed as having certain example components, additional, fewer, or different components may be used. Further, with respect to the processes discussed herein, various states may be performed in a different order, not all states are required to be reached, and fewer, additional, or different states may be utilized.

Various aspects and advantages of the embodiments have been described where appropriate. It is to be understood that not necessarily all such aspects or advantages may be achieved in accordance with any particular embodiment. Thus, for example, it should be recognized that the various embodiments may be carried out in a manner that achieves or optimizes one advantage or group of advantages as taught herein without necessarily achieving other aspects or advantages as may be taught or suggested herein. Further, embodiments may include several novel features, no single one of which is solely responsible for the embodiment's desirable attributes or which is essential to practicing the systems, devices, methods, and techniques described herein.

Claims (20)

What is claimed is:
1. A robot configured to determine its location and orientation in an environment in which a signal, external to the robot, is present, the robot comprising:
a signal sensor configured to detect a property of the signal;
a movement system configured to move the robot from a first pose comprising a first location in the environment at a first time to a second pose comprising a second location at a second time, the second location proximate to the first location;
a motion sensor configured to detect a change in location between the first pose and the second pose;
a local signal estimator configured to predict the value of the signal property at a plurality of poses, the plurality of poses comprising poses with respective predefined locations proximate to the first pose; and
a localization component configured to estimate the robot's second pose based at least in part on a value of the signal property as detected by the signal sensor at the second pose, the change in pose between the first pose and the second pose as detected by the motion sensor, and a predicted value of the signal property at the estimated second pose based at least in part on one or more of the values predicted by the local signal estimator.
2. The robot of claim 1, wherein the motion sensor is configured to detect the change in location between the first pose and the second pose by detecting a relative change in location.
3. The robot of claim 1, wherein two locations are proximate only if they are less than 2 meters apart.
4. The robot of claim 3, wherein two locations are proximate only if they are less than 2 feet apart.
5. The robot of claim 1, wherein:
the movement system is further configured to move the robot from the second location at the second time to a third pose comprising a third location in the environment at a third time, the third location not proximate to the first location;
the motion sensor is further configured to detect a change in location between the second location and the third location;
the local signal estimator is further configured to predict values of the signal property at a plurality of locations proximate to the third location based, at least in part, on extrapolating from previously predicted values at one or more locations proximate to both the first location and the third location; and
the localization component is further configured to estimate the robot's third location based at least in part on a value of the signal property as detected by the signal sensor at the third location, the change in location between the second location and the third location as detected by the motion sensor, and a value of the signal property at the estimated third location as predicted by the local signal estimator.
6. The robot of claim 5, wherein the local signal estimator is further configured to introduce noise into the extrapolation.
7. The robot of claim 1, wherein the motion sensor is further configured to detect a relative change in orientation between the first pose and the second pose.
8. The robot of claim 7, wherein the localization component is further configured to estimate the robot's second pose based at least in part on a rotational variance measure associated with how the value of the signal property detected by the signal sensor depends on the orientation of the robot.
9. The robot of claim 1, wherein the local signal estimator is further configured to detect and reject outliers of the predicted values of the signal property.
10. The robot of claim 1, wherein the estimated second pose is based at least in part on a calibration parameter of at least one of the signal sensor and the motion sensor.
11. An autonomous robot comprising:
a movement system configured to move the robot within an environment from a first pose to a second pose and from the second pose to a third pose, the first pose comprising a first location in the environment at a first time, the second pose comprising a second location at a second time, and the third pose comprising a third location at a third time, wherein the second location is proximate to the first location and the third location is not proximate to the first location;
a signal sensor configured to be responsive to a property of a signal in the environment;
a motion sensor configured to detect a change in location between the first pose and the second pose, and to detect a change in location between the second pose and the third pose;
a local signal estimator configured to predict values of the signal property at a plurality of locations proximate to the first location and to predict values of the signal property at a plurality of locations proximate to the third location, the values at the plurality of locations proximate to the third location being based, at least in part, on extrapolating from previously predicted values at one or more locations proximate to both the first location and the third location; and
a localization component configured to:
estimate the second location of the robot based at least in part on a value of the signal property as detected by the signal sensor at the second location, the change in location between the first pose and the second pose as detected by the motion sensor, and a predicted value of the signal property at the estimated second location as predicted by the local signal estimator, and to
estimate the third location of the robot based at least in part on a value of the signal property as detected by the signal sensor at the third location, the change in location between the second pose and the third pose as detected by the motion sensor, and a predicted value of the signal property at the estimated third location as predicted by the local signal estimator.
12. The robot of claim 11, wherein the localization component is further configured to estimate at least one of the second pose and the third pose based at least in part on a rotational variance measure associated with how the value of the signal property detected by the signal sensor depends on an orientation of the robot.
13. The robot of claim 11, wherein the localization component is further configured to estimate at least one of the second pose and the third pose based at least in part on a calibration parameter of at least one of the signal sensor and the motion sensor.
14. The robot of claim 11, wherein the first and third locations are not proximate in that they are greater than 2 meters apart.
15. The robot of claim 11, wherein the local signal estimator is further configured to introduce noise into the extrapolation.
16. The robot of claim 11, wherein the motion sensor is further configured to detect a relative change in orientation between the first pose and the second pose and between the second pose and the third pose.
17. The robot of claim 11, wherein the localization component is further configured to revise the second pose by applying a SLAM implementation to at least the estimated second location, the predicted value of the signal property at the estimated second location, and the value of the signal property at the estimated second location.
18. The robot of claim 11, wherein the localization component is further configured to revise the third pose by applying a SLAM implementation to at least the estimated third location, the predicted value of the signal property at the estimated third location, and the value of the signal property at the estimated third location.
19. The robot of claim 11, wherein the local signal estimator is further configured to detect and reject outliers of the predicted values of the signal property.
20. The robot of claim 11, wherein at least one of the estimated second location and the estimated third location is based at least in part on a calibration parameter of at least one of the signal sensor and the motion sensor.
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Families Citing this family (43)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005098476A1 (en) 2004-03-29 2005-10-20 Evolution Robotics, Inc. Method and apparatus for position estimation using reflected light sources
US9002511B1 (en) 2005-10-21 2015-04-07 Irobot Corporation Methods and systems for obstacle detection using structured light
WO2009038797A3 (en) * 2007-09-20 2009-05-14 Evolution Robotics Robotic game systems and methods
US8425173B2 (en) 2009-04-10 2013-04-23 Symbotic Llc Autonomous transports for storage and retrieval systems
US9026302B2 (en) 2009-11-06 2015-05-05 Irobot Corporation Methods and systems for complete coverage of a surface by an autonomous robot
US8508590B2 (en) * 2010-03-02 2013-08-13 Crown Equipment Limited Method and apparatus for simulating a physical environment to facilitate vehicle operation and task completion
US8538577B2 (en) * 2010-03-05 2013-09-17 Crown Equipment Limited Method and apparatus for sensing object load engagement, transportation and disengagement by automated vehicles
US9286810B2 (en) 2010-09-24 2016-03-15 Irobot Corporation Systems and methods for VSLAM optimization
US9008884B2 (en) 2010-12-15 2015-04-14 Symbotic Llc Bot position sensing
US9475649B2 (en) 2010-12-15 2016-10-25 Symbolic, LLC Pickface builder for storage and retrieval systems
US8694152B2 (en) 2010-12-15 2014-04-08 Symbotic, LLC Maintenance access zones for storage and retrieval systems
CA2831832A1 (en) 2011-04-11 2012-10-18 Crown Equipment Limited Method and apparatus for efficient scheduling for multiple automated non-holonomic vehicles using a coordinated path planner
US8655588B2 (en) 2011-05-26 2014-02-18 Crown Equipment Limited Method and apparatus for providing accurate localization for an industrial vehicle
US8548671B2 (en) 2011-06-06 2013-10-01 Crown Equipment Limited Method and apparatus for automatically calibrating vehicle parameters
US8589012B2 (en) 2011-06-14 2013-11-19 Crown Equipment Limited Method and apparatus for facilitating map data processing for industrial vehicle navigation
US8594923B2 (en) 2011-06-14 2013-11-26 Crown Equipment Limited Method and apparatus for sharing map data associated with automated industrial vehicles
US20140058634A1 (en) 2012-08-24 2014-02-27 Crown Equipment Limited Method and apparatus for using unique landmarks to locate industrial vehicles at start-up
US9056754B2 (en) 2011-09-07 2015-06-16 Crown Equipment Limited Method and apparatus for using pre-positioned objects to localize an industrial vehicle
KR20140067095A (en) 2011-09-09 2014-06-03 심보틱 엘엘씨 Storage and retrieval system case unit detection
US8590789B2 (en) 2011-09-14 2013-11-26 Metrologic Instruments, Inc. Scanner with wake-up mode
US8798840B2 (en) 2011-09-30 2014-08-05 Irobot Corporation Adaptive mapping with spatial summaries of sensor data
DE102011084793A1 (en) * 2011-10-19 2013-04-25 Robert Bosch Gmbh Autonomous working device
US9495018B2 (en) 2011-11-01 2016-11-15 Qualcomm Incorporated System and method for improving orientation data
US9201133B2 (en) * 2011-11-11 2015-12-01 The Board Of Trustees Of The Leland Stanford Junior University Method and system for signal-based localization
US20140031980A1 (en) * 2011-11-11 2014-01-30 Jens-Steffen Gutmann Systems and methods for extending slam to multiple regions
US8740085B2 (en) 2012-02-10 2014-06-03 Honeywell International Inc. System having imaging assembly for use in output of image data
JP5913743B2 (en) * 2012-06-08 2016-04-27 アイロボット コーポレイション Estimation of the differential sensor or carpet drift with visual measurement
CN102866706B (en) * 2012-09-13 2015-03-25 深圳市银星智能科技股份有限公司 Sweeping robot navigated by smart phone and navigation sweeping method of sweeping robot
KR20140089241A (en) * 2013-01-04 2014-07-14 한국전자통신연구원 Apparatus and Method for Creating Radio Map based on Probability for Cooperative Intelligent Robots
KR20140108821A (en) * 2013-02-28 2014-09-15 삼성전자주식회사 Mobile robot and method of localization and mapping of mobile robot
US20140288877A1 (en) * 2013-03-15 2014-09-25 Aliphcom Intermediate motion signal extraction to determine activity
US9802761B2 (en) 2013-03-15 2017-10-31 Symbotic, LLC Automated storage and retrieval system
WO2014209344A1 (en) * 2013-06-28 2014-12-31 Intel Corporation Systems and methods for revisit location detection
US9256852B1 (en) * 2013-07-01 2016-02-09 Google Inc. Autonomous delivery platform
US20150114625A1 (en) * 2013-10-29 2015-04-30 Schlumberger Technology Corporation Method of Acquiring Viscosity of A Downhole Fluid
US20150185027A1 (en) * 2014-01-02 2015-07-02 Microsoft Corporation Ground truth estimation for autonomous navigation
US9283678B2 (en) * 2014-07-16 2016-03-15 Google Inc. Virtual safety cages for robotic devices
US9701020B1 (en) * 2014-12-16 2017-07-11 Bobsweep Inc. Method and system for robotic surface coverage
GB2538779B (en) * 2015-05-28 2017-08-30 Dyson Technology Ltd A method of controlling a mobile robot
KR20160144682A (en) * 2015-06-09 2016-12-19 삼성전자주식회사 Moving robot and controlling method thereof
GB201515184D0 (en) * 2015-08-26 2015-10-07 Guidance Automation Ltd Calibrating and automated guided vehicle
US9849591B2 (en) 2015-10-02 2017-12-26 X Development Llc Localization of a robot in an environment using detected edges of a camera image from a camera of the robot and detected edges derived from a three-dimensional model of the environment
CN105334858A (en) * 2015-11-26 2016-02-17 江苏美的清洁电器股份有限公司 Floor sweeping robot and indoor map establishing method and device thereof

Citations (101)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4328545A (en) 1978-08-01 1982-05-04 Imperial Chemical Industries Limited Driverless vehicle autoguide by light signals and two directional detectors
US4584704A (en) 1984-03-01 1986-04-22 Bran Ferren Spatial imaging system
US4626995A (en) 1984-03-26 1986-12-02 Ndc Technologies, Inc. Apparatus and method for optical guidance system for automatic guided vehicle
US4628453A (en) 1983-10-17 1986-12-09 Hitachi, Ltd. Navigation apparatus for mobile system
US4638445A (en) 1984-06-08 1987-01-20 Mattaboni Paul J Autonomous mobile robot
US4638446A (en) 1983-05-31 1987-01-20 The Perkin-Elmer Corporation Apparatus and method for reducing topographical effects in an auger image
US4679152A (en) 1985-02-20 1987-07-07 Heath Company Navigation system and method for a mobile robot
US4691101A (en) 1985-06-19 1987-09-01 Hewlett-Packard Company Optical positional encoder comprising immediately adjacent detectors
US4700301A (en) 1983-11-02 1987-10-13 Dyke Howard L Method of automatically steering agricultural type vehicles
US4796198A (en) 1986-10-17 1989-01-03 The United States Of America As Represented By The United States Department Of Energy Method for laser-based two-dimensional navigation system in a structured environment
US4817000A (en) 1986-03-10 1989-03-28 Si Handling Systems, Inc. Automatic guided vehicle system
US4858132A (en) 1987-09-11 1989-08-15 Ndc Technologies, Inc. Optical navigation system for an automatic guided vehicle, and method
US4862047A (en) 1986-05-21 1989-08-29 Kabushiki Kaisha Komatsu Seisakusho Apparatus for guiding movement of an unmanned moving body
US4905151A (en) 1988-03-07 1990-02-27 Transitions Research Corporation One dimensional image visual system for a moving vehicle
US4918607A (en) 1988-09-09 1990-04-17 Caterpillar Industrial Inc. Vehicle guidance system
US4933864A (en) 1988-10-04 1990-06-12 Transitions Research Corporation Mobile robot navigation employing ceiling light fixtures
US4947094A (en) 1987-07-23 1990-08-07 Battelle Memorial Institute Optical guidance system for industrial vehicles
US5001635A (en) 1988-01-08 1991-03-19 Sanyo Electric Co., Ltd. Vehicle
US5020620A (en) 1989-09-28 1991-06-04 Tennant Company Offsetting the course of a laser guided vehicle
US5032775A (en) 1989-06-07 1991-07-16 Kabushiki Kaisha Toshiba Control apparatus for plane working robot
US5040116A (en) 1988-09-06 1991-08-13 Transitions Research Corporation Visual navigation and obstacle avoidance structured light system
US5051906A (en) 1989-06-07 1991-09-24 Transitions Research Corporation Mobile robot navigation employing retroreflective ceiling features
US5111401A (en) 1990-05-19 1992-05-05 The United States Of America As Represented By The Secretary Of The Navy Navigational control system for an autonomous vehicle
US5155684A (en) 1988-10-25 1992-10-13 Tennant Company Guiding an unmanned vehicle by reference to overhead features
US5165064A (en) 1991-03-22 1992-11-17 Cyberotics, Inc. Mobile robot guidance and navigation system
US5187662A (en) 1990-01-24 1993-02-16 Honda Giken Kogyo Kabushiki Kaisha Steering control system for moving vehicle
JPH05257527A (en) 1992-03-13 1993-10-08 Shinko Electric Co Ltd Detection of position and direction of unmanned vehicle
US5307271A (en) 1990-09-28 1994-04-26 The United States Of America As Represented By The Secretary Of The Navy Reflexive teleoperated control system for a remotely controlled vehicle
US5321614A (en) 1991-06-06 1994-06-14 Ashworth Guy T D Navigational control apparatus and method for autonomus vehicles
US5453931A (en) 1994-10-25 1995-09-26 Watts, Jr.; James R. Navigating robot with reference line plotter
US5510893A (en) 1993-08-18 1996-04-23 Digital Stream Corporation Optical-type position and posture detecting device
US5525883A (en) 1994-07-08 1996-06-11 Sara Avitzour Mobile robot location determination employing error-correcting distributed landmarks
US5677836A (en) 1994-03-11 1997-10-14 Siemens Aktiengesellschaft Method for producing a cellularly structured environment map of a self-propelled, mobile unit that orients itself in the environment at least with the assistance of sensors based on wave refection
US5717484A (en) 1994-03-22 1998-02-10 Minolta Co., Ltd. Position detecting system
US5770936A (en) 1992-06-18 1998-06-23 Kabushiki Kaisha Yaskawa Denki Noncontacting electric power transfer apparatus, noncontacting signal transfer apparatus, split-type mechanical apparatus employing these transfer apparatus, and a control method for controlling same
US5911767A (en) 1994-10-04 1999-06-15 Garibotto; Giovanni Navigation system for an autonomous mobile robot
US5942869A (en) 1997-02-13 1999-08-24 Honda Giken Kogyo Kabushiki Kaisha Mobile robot control device
US5995884A (en) 1997-03-07 1999-11-30 Allen; Timothy P. Computer peripheral floor cleaning system and navigation method
US6009359A (en) 1996-09-18 1999-12-28 National Research Council Of Canada Mobile system for indoor 3-D mapping and creating virtual environments
US6076025A (en) 1997-01-29 2000-06-13 Honda Giken Kogyo K.K. Mobile robot steering method and control device
US6108076A (en) 1998-12-21 2000-08-22 Trimble Navigation Limited Method and apparatus for accurately positioning a tool on a mobile machine using on-board laser and positioning system
US6205380B1 (en) 1996-07-02 2001-03-20 Siemens Aktiengesellschaft Process for preparing an area plan having a cellular structure and comprising a unit moving automatically and positioned in said area using sensors based on wave reflection
US6292712B1 (en) 1998-01-29 2001-09-18 Northrop Grumman Corporation Computer interface system for a robotic system
US6339735B1 (en) 1998-12-29 2002-01-15 Friendly Robotics Ltd. Method for operating a robot
US20020027652A1 (en) 2000-06-29 2002-03-07 Paromtchik Igor E. Method for instructing target position for mobile body, method for controlling transfer thereof, and method as well as system of optical guidance therefor
US6370453B2 (en) 1998-07-31 2002-04-09 Volker Sommer Service robot for the automatic suction of dust from floor surfaces
US20020060542A1 (en) 2000-11-22 2002-05-23 Jeong-Gon Song Mobile robot system using RF module
US6459955B1 (en) 1999-11-18 2002-10-01 The Procter & Gamble Company Home cleaning robot
US6457206B1 (en) 2000-10-20 2002-10-01 Scott H. Judson Remote-controlled vacuum cleaner
US6493612B1 (en) 1998-12-18 2002-12-10 Dyson Limited Sensors arrangement
US6496754B2 (en) 2000-11-17 2002-12-17 Samsung Kwangju Electronics Co., Ltd. Mobile robot and course adjusting method thereof
US6496755B2 (en) 1999-11-24 2002-12-17 Personal Robotics, Inc. Autonomous multi-platform robot system
US20030090522A1 (en) 2001-11-09 2003-05-15 Asm International Nv Graphical representation of a wafer processing process
US6574536B1 (en) 1996-01-29 2003-06-03 Minolta Co., Ltd. Moving apparatus for efficiently moving on floor with obstacle
US20030120389A1 (en) 2001-09-26 2003-06-26 F Robotics Acquisitions Ltd. Robotic vacuum cleaner
US6597076B2 (en) 1999-06-11 2003-07-22 Abb Patent Gmbh System for wirelessly supplying a large number of actuators of a machine with electrical power
US6594844B2 (en) 2000-01-24 2003-07-22 Irobot Corporation Robot obstacle detection system
US6658325B2 (en) 2001-01-16 2003-12-02 Stephen Eliot Zweig Mobile robotic with web server and digital radio links
US6677938B1 (en) 1999-08-04 2004-01-13 Trimble Navigation, Ltd. Generating positional reality using RTK integrated with scanning lasers
US6690134B1 (en) 2001-01-24 2004-02-10 Irobot Corporation Method and system for robot localization and confinement
US6732826B2 (en) 2001-04-18 2004-05-11 Samsung Gwangju Electronics Co., Ltd. Robot cleaner, robot cleaning system and method for controlling same
US20040201361A1 (en) 2003-04-09 2004-10-14 Samsung Electronics Co., Ltd. Charging system for robot
US20040204792A1 (en) 2003-03-14 2004-10-14 Taylor Charles E. Robotic vacuum with localized cleaning algorithm
US6809490B2 (en) 2001-06-12 2004-10-26 Irobot Corporation Method and system for multi-mode coverage for an autonomous robot
US20040220707A1 (en) 2003-05-02 2004-11-04 Kim Pallister Method, apparatus and system for remote navigation of robotic devices
US6830120B1 (en) 1996-01-25 2004-12-14 Penguin Wax Co., Ltd. Floor working machine with a working implement mounted on a self-propelled vehicle for acting on floor
US20050000543A1 (en) 2003-03-14 2005-01-06 Taylor Charles E. Robot vacuum with internal mapping system
US6883201B2 (en) 2002-01-03 2005-04-26 Irobot Corporation Autonomous floor-cleaning robot
US20050171636A1 (en) 2004-01-30 2005-08-04 Funai Electric Co., Ltd. Autonomous mobile robot cleaner system
US20050194973A1 (en) 2004-02-04 2005-09-08 Samsung Electronics Co., Ltd Method and apparatus for generating magnetic field map and method and apparatus for checking pose of mobile body using the magnetic field map
US20050204505A1 (en) 2004-02-04 2005-09-22 Funai Electric Co, Ltd. Autonomous vacuum cleaner and autonomous vacuum cleaner network system
USD510066S1 (en) 2004-05-05 2005-09-27 Irobot Corporation Base station for robot
US20050213109A1 (en) 2004-03-29 2005-09-29 Evolution Robotics, Inc. Sensing device and method for measuring position and orientation relative to multiple light sources
US20050213082A1 (en) 2004-03-29 2005-09-29 Evolution Robotics, Inc. Methods and apparatus for position estimation using reflected light sources
US6956348B2 (en) 2004-01-28 2005-10-18 Irobot Corporation Debris sensor for cleaning apparatus
US7024278B2 (en) 2002-09-13 2006-04-04 Irobot Corporation Navigational control system for a robotic device
US7053578B2 (en) 2002-07-08 2006-05-30 Alfred Kaercher Gmbh & Co. Kg Floor treatment system
US20060165276A1 (en) * 2005-01-25 2006-07-27 Samsung Electronics Co., Ltd Apparatus and method for estimating location of mobile body and generating map of mobile body environment using upper image of mobile body environment, and computer readable recording medium storing computer program controlling the apparatus
US7155308B2 (en) 2000-01-24 2006-12-26 Irobot Corporation Robot obstacle detection system
US20060293788A1 (en) 2005-06-26 2006-12-28 Pavel Pogodin Robotic floor care appliance with improved remote management
US20070061043A1 (en) 2005-09-02 2007-03-15 Vladimir Ermakov Localization and mapping system and method for a robotic device
US20070106423A1 (en) * 2005-11-07 2007-05-10 Samsung Electronics Co. Ltd. Robot and method of localizing the same
US20080039974A1 (en) 2006-03-17 2008-02-14 Irobot Corporation Robot Confinement
US7332890B2 (en) 2004-01-21 2008-02-19 Irobot Corporation Autonomous robot auto-docking and energy management systems and methods
US20080266748A1 (en) 2004-07-29 2008-10-30 Hyung-Joo Lee Amplification Relay Device of Electromagnetic Wave and a Radio Electric Power Conversion Apparatus Using the Above Device
US20090102296A1 (en) 2007-01-05 2009-04-23 Powercast Corporation Powering cell phones and similar devices using RF energy harvesting
US20100001991A1 (en) 2008-07-07 2010-01-07 Samsung Electronics Co., Ltd. Apparatus and method of building map for mobile robot
US20100082193A1 (en) 2004-07-07 2010-04-01 Mark Joseph Chiappetta Celestial navigation system for an autonomous vehicle
US7706917B1 (en) 2004-07-07 2010-04-27 Irobot Corporation Celestial navigation system for an autonomous robot
US20100110412A1 (en) * 2008-10-31 2010-05-06 Honeywell International Inc. Systems and methods for localization and mapping using landmarks detected by a measurement device
US20100274387A1 (en) 2009-04-24 2010-10-28 Robert Bosch Gmbh Method of accurate mapping with mobile robots
US20100315288A1 (en) * 2009-06-15 2010-12-16 Zhang Liu Tracking Arrangement for a Communications System on a Mobile Platform
US7860680B2 (en) 2002-03-07 2010-12-28 Microstrain, Inc. Robotic system for powering and interrogating sensors
US20110054689A1 (en) * 2009-09-03 2011-03-03 Battelle Energy Alliance, Llc Robots, systems, and methods for hazard evaluation and visualization
US8086419B2 (en) 2002-12-17 2011-12-27 Evolution Robotics, Inc. Systems and methods for adding landmarks for visual simultaneous localization and mapping
US8087117B2 (en) 2006-05-19 2012-01-03 Irobot Corporation Cleaning robot roller processing
US20120213443A1 (en) * 2009-10-30 2012-08-23 Yujin Robot Co., Ltd. Map generating and updating method for mobile robot position recognition
US20120219207A1 (en) * 2009-10-30 2012-08-30 Yujin Robot Co., Ltd. Slip detection apparatus and method for a mobile robot
US8386081B2 (en) 2002-09-13 2013-02-26 Irobot Corporation Navigational control system for a robotic device
US8396599B2 (en) 2004-11-02 2013-03-12 Kabushiki Kaisha Yaskawa Denki Robot control apparatus and robot system
US9008835B2 (en) 2004-06-24 2015-04-14 Irobot Corporation Remote control scheduler and method for autonomous robotic device

Family Cites Families (1072)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
NL28010C (en) 1928-01-03
US1780221A (en) 1930-05-08 1930-11-04 Buchmann John Brush
FR722755A (en) 1930-09-09 1932-03-25 Machine for dust, stain removal and cleaning of floors and carpets laid
US1970302A (en) 1932-09-13 1934-08-14 Charles C Gerhardt Brush
US2136324A (en) 1934-09-05 1938-11-08 Simon Louis John Apparatus for cleansing floors and like surfaces
US2302111A (en) 1940-11-26 1942-11-17 Air Way Electric Appl Corp Vacuum cleaner
US2353621A (en) 1941-10-13 1944-07-11 Ohio Citizens Trust Company Dust indicator for air-method cleaning systems
US2770825A (en) 1951-09-10 1956-11-20 Bissell Carpet Sweeper Co Carpet sweeper and brush cleaning combs therefor
GB702426A (en) 1951-12-28 1954-01-13 Bissell Carpet Sweeper Co Improvements in or relating to carpet sweepers
US2930055A (en) 1957-12-16 1960-03-29 Burke R Fallen Floor wax dispensing and spreading unit
US3888181A (en) 1959-09-10 1975-06-10 Us Army Munition control system
US3119369A (en) * 1960-12-28 1964-01-28 Ametek Inc Device for indicating fluid flow
US3166138A (en) * 1961-10-26 1965-01-19 Jr Edward D Dunn Stair climbing conveyance
US3550714A (en) 1964-10-20 1970-12-29 Mowbot Inc Lawn mower
US3375375A (en) 1965-01-08 1968-03-26 Honeywell Inc Orientation sensing means comprising photodetectors and projected fans of light
US3381652A (en) 1965-10-21 1968-05-07 Nat Union Electric Corp Visual-audible alarm for a vacuum cleaner
DE1503746B1 (en) 1965-12-23 1970-01-22 Bissell Gmbh carpet sweeper
US3333564A (en) 1966-06-28 1967-08-01 Sunbeam Corp Vacuum bag indicator
US3569727A (en) 1968-09-30 1971-03-09 Bendix Corp Control means for pulse generating apparatus
FR2022830A1 (en) 1968-11-08 1970-08-07 Electrolux Ab
US3649981A (en) 1970-02-25 1972-03-21 Wayne Manufacturing Co Curb travelling sweeper vehicle
US3674316A (en) 1970-05-14 1972-07-04 Robert J De Brey Particle monitor
US3989311A (en) 1970-05-14 1976-11-02 Debrey Robert J Particle monitoring apparatus
US3845831A (en) 1970-08-11 1974-11-05 Martin C Vehicle for rough and muddy terrain
US3690559A (en) 1970-09-16 1972-09-12 Robert H Rudloff Tractor mounted pavement washer
DE2049136A1 (en) 1970-10-07 1972-04-13 Bosch Gmbh Robert
CA908697A (en) 1971-01-21 1972-08-29 Bombardier Jerome Suspension for tracked vehicles
ES403465A1 (en) 1971-05-26 1975-05-01 Tecneco Spa Improvements in measuring devices opacidadde fluids, particularly smoke.
US3678882A (en) 1971-05-28 1972-07-25 Nat Union Electric Corp Combination alarm and filter bypass device for a suction cleaner
DE2128842C3 (en) 1971-06-11 1980-12-18 Robert Bosch Gmbh, 7000 Stuttgart
GB1381237A (en) 1972-02-11 1975-01-22 Electrolux Ltd Vacuum cleaner or other apparatus having a dust collector and a signal device
US4175892A (en) 1972-05-10 1979-11-27 Brey Robert J De Particle monitor
US3809004A (en) 1972-09-18 1974-05-07 W Leonheart All terrain vehicle
DE2262648B2 (en) * 1972-12-21 1975-01-09 Hermann 7000 Stuttgart Haaga
US3863285A (en) * 1973-07-05 1975-02-04 Hiroshi Hukuba Carpet sweeper
US3851349A (en) 1973-09-26 1974-12-03 Clarke Gravely Corp Floor scrubber flow divider
GB1473109A (en) 1973-10-05 1977-05-11
US4119900A (en) 1973-12-21 1978-10-10 Ito Patent-Ag Method and system for the automatic orientation and control of a robot
GB1477066A (en) * 1974-09-10 1977-06-22 Ceccato & Co Rotary brush for vehicle washing
JPS5321869Y2 (en) 1974-11-08 1978-06-07
US4012681A (en) 1975-01-03 1977-03-15 Curtis Instruments, Inc. Battery control system for battery operated vehicles
US3989931A (en) 1975-05-19 1976-11-02 Rockwell International Corporation Pulse count generator for wide range digital phase detector
GB1505778A (en) * 1975-08-20 1978-03-30 Electrolux Ltd Vacuum cleaner a dust container therefor and a method of making the dust container
US4099284A (en) 1976-02-20 1978-07-11 Tanita Corporation Hand sweeper for carpets
JPS5316183A (en) 1976-07-28 1978-02-14 Hitachi Ltd Fluid pressure driving device
JPS53110257U (en) 1977-02-07 1978-09-04
US4618213A (en) 1977-03-17 1986-10-21 Applied Elastomerics, Incorporated Gelatinous elastomeric optical lens, light pipe, comprising a specific block copolymer and an oil plasticizer
US4199838A (en) 1977-09-15 1980-04-29 Aktiebolaget Electrolux Indicating device for vacuum cleaners
US4198727A (en) 1978-01-19 1980-04-22 Farmer Gary L Baseboard dusters for vacuum cleaners
FR2416480B1 (en) 1978-02-03 1982-02-26 Thomson Csf
US4196727A (en) 1978-05-19 1980-04-08 Becton, Dickinson And Company See-through anesthesia mask
EP0007790A1 (en) * 1978-08-01 1980-02-06 Imperial Chemical Industries Plc Driverless vehicle carrying non-directional detectors auto-guided by light signals
GB2038615B (en) 1978-12-31 1983-04-13 Nintendo Co Ltd Self-moving type vacuum cleaner
US5164579A (en) 1979-04-30 1992-11-17 Diffracto Ltd. Method and apparatus for electro-optically determining the dimension, location and attitude of objects including light spot centroid determination
US4373804A (en) 1979-04-30 1983-02-15 Diffracto Ltd. Method and apparatus for electro-optically determining the dimension, location and attitude of objects
US4297578A (en) 1980-01-09 1981-10-27 Carter William R Airborne dust monitor
US4367403A (en) * 1980-01-21 1983-01-04 Rca Corporation Array positioning system with out-of-focus solar cells
US4305234A (en) 1980-02-04 1981-12-15 Flo-Pac Corporation Composite brush
US4492058A (en) * 1980-02-14 1985-01-08 Adolph E. Goldfarb Ultracompact miniature toy vehicle with four-wheel drive and unusual climbing capability
US4369543A (en) * 1980-04-14 1983-01-25 Jen Chen Remote-control radio vacuum cleaner
JPH0560049B2 (en) 1980-07-01 1993-09-01 Minolta Camera Kk
JPS595315Y2 (en) 1980-09-13 1984-02-17
JPS6031611Y2 (en) 1980-10-03 1985-09-21
JPS6254951B2 (en) 1980-10-21 1987-11-17 Nagasawa Seisakusho
US4401909A (en) 1981-04-03 1983-08-30 Dickey-John Corporation Grain sensor using a piezoelectric element
US4416033A (en) 1981-10-08 1983-11-22 The Hoover Company Full bag indicator
US4652917A (en) 1981-10-28 1987-03-24 Honeywell Inc. Remote attitude sensor using single camera and spiral patterns
US4769700A (en) 1981-11-20 1988-09-06 Diffracto Ltd. Robot tractors
US4482960A (en) 1981-11-20 1984-11-13 Diffracto Ltd. Robot tractors
JPS58100840A (en) 1981-12-12 1983-06-15 Canon Inc Finder of camera
EP0099489B1 (en) 1982-07-05 1986-11-26 Sommer, Schenk AG Method of and machine for cleaning a water basin
JPH0147967B2 (en) 1982-07-13 1989-10-17 Kubota Ltd
GB2128842B (en) 1982-08-06 1986-04-16 Univ London Method of presenting visual information
US4445245A (en) 1982-08-23 1984-05-01 Lu Ning K Surface sweeper
JPS5933511U (en) 1982-08-24 1984-03-01
US4624026A (en) 1982-09-10 1986-11-25 Tennant Company Surface maintenance machine with rotary lip
US4556313A (en) 1982-10-18 1985-12-03 United States Of America As Represented By The Secretary Of The Army Short range optical rangefinder
JPS5994005U (en) 1982-12-16 1984-06-26
JPS59112311A (en) 1982-12-20 1984-06-28 Komatsu Ltd Guiding method of unmanned moving body
JPS5999308U (en) 1982-12-23 1984-07-05
JPS59120124A (en) 1982-12-28 1984-07-11 Matsushita Electric Ind Co Ltd Electric cleaner
JPH0346290Y2 (en) 1983-02-24 1991-09-30
US4481692A (en) 1983-03-29 1984-11-13 Gerhard Kurz Operating-condition indicator for vacuum cleaners
JPS59184917A (en) 1983-04-05 1984-10-20 Tsubakimoto Chain Co Guiding method of unmanned truck
US4575211A (en) 1983-04-18 1986-03-11 Canon Kabushiki Kaisha Distance measuring device
JPS59164973U (en) 1983-04-20 1984-11-05
DE3317376C2 (en) 1983-05-13 1987-12-03 Diehl Gmbh & Co, 8500 Nuernberg, De
JPS59212924A (en) 1983-05-17 1984-12-01 Mitsubishi Electric Corp Position detector for traveling object
US4477998A (en) 1983-05-31 1984-10-23 You Yun Long Fantastic wall-climbing toy
JPH0379157B2 (en) 1983-06-07 1991-12-17 Kobe Steel Ltd
US4513469A (en) 1983-06-13 1985-04-30 Godfrey James O Radio controlled vacuum cleaner
JPS6089213A (en) 1983-10-19 1985-05-20 Komatsu Ltd Detecting method for position and direction of unmanned truck
DE3478824D1 (en) 1983-10-26 1989-08-03 Automax Kk Control system for mobile robot
JPS60118912U (en) * 1984-01-18 1985-08-12
DE3404202C2 (en) 1984-02-07 1992-12-17 Wegmann & Co Gmbh, 3500 Kassel, De
DE3431175C2 (en) 1984-02-08 1986-01-09 Gerhard 7262 Althengstett De Kurz
DE3431164C2 (en) 1984-02-08 1987-10-08 Gerhard 7262 Althengstett De Kurz
US4712740A (en) 1984-03-02 1987-12-15 The Regina Co., Inc. Venturi spray nozzle for a cleaning device
JPS6123221Y2 (en) 1984-04-04 1986-07-11
JPS60211510A (en) 1984-04-05 1985-10-23 Komatsu Ltd Position detecting method of mobile body
DE3413793A1 (en) 1984-04-12 1985-10-24 Bbc Brown Boveri & Cie Drive for a switch
US4832098A (en) 1984-04-16 1989-05-23 The Uniroyal Goodrich Tire Company Non-pneumatic tire with supporting and cushioning members
US4620285A (en) 1984-04-24 1986-10-28 Heath Company Sonar ranging/light detection system for use in a robot
US4649504A (en) 1984-05-22 1987-03-10 Cae Electronics, Ltd. Optical position and orientation measurement techniques
US4703820A (en) 1984-05-31 1987-11-03 Imperial Chemical Industries, Plc Vehicle guidance means
JPS60259895A (en) 1984-06-04 1985-12-21 Toshiba Corp Multi tube type super heat steam returning device
JPS6170407A (en) 1984-08-08 1986-04-11 Canon Inc Instrument for measuring distance
JPS6197711A (en) 1984-10-18 1986-05-16 Casio Comput Co Ltd Infrared-ray tracking robot system
JPS6197712A (en) 1984-10-18 1986-05-16 Casio Comput Co Ltd Target of infrared-ray tracking robot
EP0182754A1 (en) 1984-11-21 1986-05-28 Alfredo Cavalli (deceased) Multi-purpose household appliance particularly for cleaning floors, carpets, laid carpeting, and the like
GB8502506D0 (en) 1985-01-31 1985-03-06 Emi Ltd Smoke detector
JPH0554128B2 (en) 1985-02-18 1993-08-11 Toyoda Machine Works Ltd
JPS61160366U (en) 1985-03-27 1986-10-04
US4748336A (en) 1985-05-01 1988-05-31 Nippondenso Co., Ltd. Optical dust detector assembly for use in an automotive vehicle
FR2583701B1 (en) 1985-06-21 1990-03-23 Commissariat Energie Atomique Vehicle has tracked variable geometry
JPH0424450B2 (en) 1985-06-21 1992-04-27 Murata Machinery Ltd
WO1987000265A1 (en) * 1985-06-28 1987-01-15 Moorhouse, D., J. Detonator actuator
US4662854A (en) 1985-07-12 1987-05-05 Union Electric Corp. Self-propellable toy and arrangement for and method of controlling the movement thereof
DE3660742D1 (en) * 1985-07-26 1988-10-27 Dulevo Spa A floor and bounded surface sweeper machine
US4811228A (en) 1985-09-17 1989-03-07 Inik Instrument Och Elektronik Method of navigating an automated guided vehicle
JPH0547601B2 (en) 1985-09-27 1993-07-19 Kawasaki Heavy Ind Ltd
DE3534621A1 (en) 1985-09-28 1987-04-02 Interlava Ag vacuum cleaner
JPH0421069Y2 (en) * 1985-09-30 1992-05-14
US4700427A (en) 1985-10-17 1987-10-20 Knepper Hans Reinhard Method of automatically steering self-propelled floor-cleaning machines and floor-cleaning machine for practicing the method
JPH0319408Y2 (en) 1985-10-19 1991-04-24
JPS6270709U (en) 1985-10-22 1987-05-06
JPH0582602B2 (en) 1985-11-21 1993-11-19 Hitachi Ltd
US4909972A (en) 1985-12-02 1990-03-20 Britz Johannes H Method and apparatus for making a solid foamed tire core
FR2591329B1 (en) 1985-12-10 1992-05-22 Canon Kk Apparatus and three-dimensional information processing method
JPH0465231B2 (en) * 1985-12-28 1992-10-19 Aisan Ind
US4654924A (en) 1985-12-31 1987-04-07 Whirlpool Corporation Microcomputer control system for a canister vacuum cleaner
EP0231419A1 (en) 1986-02-05 1987-08-12 Interlava AG Indicating and function controlling optical unit for a vacuum cleaner
JPS62154008U (en) 1986-03-19 1987-09-30
GB8607365D0 (en) 1986-03-25 1986-04-30 Roneo Alcatel Ltd Electromechanical drives
JPS62164431U (en) 1986-04-08 1987-10-19
JPH0782385B2 (en) 1986-05-12 1995-09-06 三洋電機株式会社 Guidance system of the moving body
JPS62263508A (en) 1986-05-12 1987-11-16 Sanyo Electric Co Ltd Autonomous type work track
US4777416A (en) 1986-05-16 1988-10-11 Denning Mobile Robotics, Inc. Recharge docking system for mobile robot
US4710020A (en) 1986-05-16 1987-12-01 Denning Mobil Robotics, Inc. Beacon proximity detection system for a vehicle
US4829442A (en) 1986-05-16 1989-05-09 Denning Mobile Robotics, Inc. Beacon navigation system and method for guiding a vehicle
JPS62189057U (en) 1986-05-22 1987-12-01
US4955714A (en) 1986-06-26 1990-09-11 Stotler James G System for simulating the appearance of the night sky inside a room
US4752799A (en) 1986-07-07 1988-06-21 Honeywell Inc. Optical proximity sensing optics
FR2601443B1 (en) 1986-07-10 1991-11-29 Centre Nat Etd Spatiales Position sensor and its application to the telemetry, in particular for space robotics
JPH07102204B2 (en) 1986-09-25 1995-11-08 株式会社マキタ Burashikuri - Na
FI74829C (en) 1986-10-01 1988-03-10 Allaway Oy Foerfarande Foer controlling the operation of a anlaeggning saosom dammsugare, centraldammsugare, maskinellt luftkonditioneringssystem or the like.
KR940002923B1 (en) 1986-10-08 1994-04-07 미타 가츠시게 Method and apparatus for operating vacuum cleaner
US4920060A (en) 1986-10-14 1990-04-24 Hercules Incorporated Device and process for mixing a sample and a diluent
JPS6371857U (en) 1986-10-28 1988-05-13
EP0265542A1 (en) 1986-10-28 1988-05-04 Richard R. Rathbone Optical navigation system
ES2035866T3 (en) 1986-10-30 1993-05-01 Institute For Industrial Research And Standards System for detecting the position of a moving object.
US4733431A (en) 1986-12-09 1988-03-29 Whirlpool Corporation Vacuum cleaner with performance monitoring system
FR2620070A2 (en) 1986-12-11 1989-03-10 Jonas Andre mobile unit self-guided and cleaning apparatus such as a vacuum cleaner comprising such a unit
US4735136A (en) 1986-12-23 1988-04-05 Whirlpool Corporation Full receptacle indicator for compactor
CA1311852C (en) 1987-01-09 1992-12-22 James R. Allard Knowledge acquisition tool for automated knowledge extraction
JPH023754B2 (en) 1987-02-18 1990-01-24 Shingijutsu Kaihatsu Jigyodan
US4855915A (en) 1987-03-13 1989-08-08 Dallaire Rodney J Autoguided vehicle using reflective materials
JPH0786767B2 (en) 1987-03-30 1995-09-20 株式会社日立製作所 Travel control method of the self-propelled robot
KR900003080B1 (en) 1987-03-30 1990-05-07 다니이 아끼오 Nozzle of electric-cleaners
US4818875A (en) 1987-03-30 1989-04-04 The Foxboro Company Portable battery-operated ambient air analyzer
DK172087D0 (en) 1987-04-03 1987-04-03 Rotowash Scandinavia An apparatus for wet cleaning of floor or wall surfaces
JPS63158032U (en) 1987-04-03 1988-10-17
JP2606842B2 (en) 1987-05-30 1997-05-07 東芝エー・ブイ・イー 株式会社 Vacuum cleaner
DE3886267D1 (en) 1987-06-01 1994-01-27 Electro Optics Ind Ltd An arrangement for measuring angular displacement of an object.
DE3888732T2 (en) 1987-06-22 1994-08-11 Arnex Hb Method and apparatus for laser-optical navigation.
US4846297A (en) 1987-09-28 1989-07-11 Tennant Company Automated guided vehicle
ES2102346T3 (en) 1987-10-16 1997-08-01 Matsushita Electric Ind Co Ltd Suction machine.
GB8728508D0 (en) 1987-12-05 1988-01-13 Brougham Pickard J G Accessory unit for vacuum cleaner
DE3779649D1 (en) 1987-12-16 1992-07-09 Hako Gmbh & Co Guided hand sweeper.
US5024529A (en) 1988-01-29 1991-06-18 Synthetic Vision Systems, Inc. Method and system for high-speed, high-resolution, 3-D imaging of an object at a vision station
US5002145A (en) 1988-01-29 1991-03-26 Nec Corporation Method and apparatus for controlling automated guided vehicle
US4891762A (en) * 1988-02-09 1990-01-02 Chotiros Nicholas P Method and apparatus for tracking, mapping and recognition of spatial patterns
DE3803824C2 (en) 1988-02-09 1991-10-31 Interlava Ag, Lugano, Ch
US4782550A (en) 1988-02-12 1988-11-08 Von Schrader Company Automatic surface-treating apparatus
US4851661A (en) 1988-02-26 1989-07-25 The United States Of America As Represented By The Secretary Of The Navy Programmable near-infrared ranging system
DE3812633C2 (en) 1988-04-15 1991-05-16 Daimler-Benz Aktiengesellschaft, 7000 Stuttgart, De
US4919489A (en) 1988-04-20 1990-04-24 Grumman Aerospace Corporation Cog-augmented wheel for obstacle negotiation
JP2583958B2 (en) * 1988-04-20 1997-02-19 ナショナルタイヤ株式会社 Floor nozzle for a vacuum cleaner
US4977618A (en) 1988-04-21 1990-12-11 Photonics Corporation Infrared data communications
US4919224A (en) 1988-05-16 1990-04-24 Industrial Technology Research Institute Automatic working vehicular system
JPH01175669U (en) 1988-05-23 1989-12-14
US4887415A (en) 1988-06-10 1989-12-19 Martin Robert L Automated lawn mower or floor polisher
KR910006887B1 (en) 1988-06-15 1991-09-10 다니이 아끼오 Dust detector for vacuum cleaner
JPH026312U (en) 1988-06-27 1990-01-17
GB8817039D0 (en) 1988-07-18 1988-08-24 Martecon Uk Ltd Improvements in/relating to polymer filled tyres
US4857912A (en) 1988-07-27 1989-08-15 The United States Of America As Represented By The Secretary Of The Navy Intelligent security assessment system
US5127128A (en) 1989-07-27 1992-07-07 Goldstar Co., Ltd. Cleaner head
US4977639A (en) 1988-08-15 1990-12-18 Mitsubishi Denki Kabushiki Kaisha Floor detector for vacuum cleaners
US4954962A (en) 1988-09-06 1990-09-04 Transitions Research Corporation Visual navigation and obstacle avoidance structured light system
US4932831A (en) 1988-09-26 1990-06-12 Remotec, Inc. All terrain mobile robot
JPH0546239Y2 (en) 1988-10-31 1993-12-02
JPH0779791B2 (en) 1988-11-07 1995-08-30 松下電器産業株式会社 Vacuum cleaner
GB8826772D0 (en) 1988-11-16 1988-12-21 Unilever Plc Nozzle system
JPH0824652B2 (en) 1988-12-06 1996-03-13 松下電器産業株式会社 Vacuum cleaner
DE3914306C2 (en) 1988-12-16 1991-08-08 Interlava Ag, Lugano, Ch
EP0374614B1 (en) * 1988-12-21 1993-08-11 PIRELLI CAVI S.p.A. Method and optical sensor for determining the position of a mobile body
US4918441A (en) 1988-12-22 1990-04-17 Ford New Holland, Inc. Non-contact sensing unit for row crop harvester guidance system
US4893025A (en) * 1988-12-30 1990-01-09 Us Administrat Distributed proximity sensor system having embedded light emitters and detectors
US4962453A (en) 1989-02-07 1990-10-09 Transitions Research Corporation Autonomous vehicle for working on a surface and method of controlling same
US4967862A (en) 1989-03-13 1990-11-06 Transitions Research Corporation Tether-guided vehicle and method of controlling same
JP2815606B2 (en) 1989-04-25 1998-10-27 株式会社トキメック Control system of concrete floor finishing robot
US4971591A (en) 1989-04-25 1990-11-20 Roni Raviv Vehicle with vacuum traction
US5154617A (en) 1989-05-09 1992-10-13 Prince Corporation Modular vehicle electronic system
US5182833A (en) * 1989-05-11 1993-02-02 Matsushita Electric Industrial Co., Ltd. Vacuum cleaner
FR2648071B1 (en) 1989-06-07 1995-05-19 Onet Method and apparatus for autonomous automatic floor cleaning by running scheduled tasks
JPH03129328A (en) 1989-06-27 1991-06-03 Victor Co Of Japan Ltd Electromagnetic radiation flux scanning device and display device
US4961303A (en) 1989-07-10 1990-10-09 Ford New Holland, Inc. Apparatus for opening conditioning rolls
US5144715A (en) 1989-08-18 1992-09-08 Matsushita Electric Industrial Co., Ltd. Vacuum cleaner and method of determining type of floor surface being cleaned thereby
US5002501A (en) 1989-10-02 1991-03-26 Raychem Corporation Electrical plug
US4961304A (en) 1989-10-20 1990-10-09 J. I. Case Company Cotton flow monitoring system for a cotton harvester
US5045769A (en) 1989-11-14 1991-09-03 The United States Of America As Represented By The Secretary Of The Navy Intelligent battery charging system
US5033291A (en) 1989-12-11 1991-07-23 Tekscan, Inc. Flexible tactile sensor for measuring foot pressure distributions and for gaskets
JP2714588B2 (en) 1989-12-13 1998-02-16 株式会社ブリヂストン Tire inspection apparatus
US5070567A (en) 1989-12-15 1991-12-10 Neta Holland Electrically-driven brush
JPH03186243A (en) 1989-12-15 1991-08-14 Matsushita Electric Ind Co Ltd Upright type vacuum cleaner
US5063846A (en) 1989-12-21 1991-11-12 Hughes Aircraft Company Modular, electronic safe-arm device
US5093956A (en) 1990-01-12 1992-03-10 Royal Appliance Mfg. Co. Snap-together housing
US5647554A (en) 1990-01-23 1997-07-15 Sanyo Electric Co., Ltd. Electric working apparatus supplied with electric power through power supply cord
US5115538A (en) 1990-01-24 1992-05-26 Black & Decker Inc. Vacuum cleaners
US5084934A (en) * 1990-01-24 1992-02-04 Black & Decker Inc. Vacuum cleaners
US5020186A (en) 1990-01-24 1991-06-04 Black & Decker Inc. Vacuum cleaners
US4956891A (en) 1990-02-21 1990-09-18 Castex Industries, Inc. Floor cleaner
JP3149430B2 (en) 1990-02-22 2001-03-26 松下電器産業株式会社 Upright vacuum cleaner
US5049802A (en) 1990-03-01 1991-09-17 Caterpillar Industrial Inc. Charging system for a vehicle
ES2072472T3 (en) 1990-04-10 1995-07-16 Matsushita Electric Ind Co Ltd Aspirator driven control.
US5018240A (en) 1990-04-27 1991-05-28 Cimex Limited Carpet cleaner
US5170352A (en) 1990-05-07 1992-12-08 Fmc Corporation Multi-purpose autonomous vehicle with path plotting
US5142985A (en) 1990-06-04 1992-09-01 Motorola, Inc. Optical detection device
US5109566A (en) 1990-06-28 1992-05-05 Matsushita Electric Industrial Co., Ltd. Self-running cleaning apparatus
JPH04227507A (en) 1990-07-02 1992-08-17 Duranwhite Hugh Method for generating and holding map for moving robot
US5307273A (en) 1990-08-29 1994-04-26 Goldstar Co., Ltd. Apparatus and method for recognizing carpets and stairs by cleaning robot
US5093955A (en) 1990-08-29 1992-03-10 Tennant Company Combined sweeper and scrubber
EP0550473B1 (en) 1990-09-24 1996-12-11 André COLENS Continuous, self-contained mowing system
US5202742A (en) 1990-10-03 1993-04-13 Aisin Seiki Kabushiki Kaisha Laser radar for a vehicle lateral guidance system
US5086535A (en) * 1990-10-22 1992-02-11 Racine Industries, Inc. Machine and method using graphic data for treating a surface
US5204814A (en) 1990-11-13 1993-04-20 Mobot, Inc. Autonomous lawn mower
JPH0542088A (en) 1990-11-26 1993-02-23 Matsushita Electric Ind Co Ltd Controller for electric system
JPH0824655B2 (en) 1990-11-26 1996-03-13 松下電器産業株式会社 Vacuum cleaner
KR930000081B1 (en) 1990-12-07 1993-01-08 이헌조 Cleansing method of electric vacuum cleaner
US5136675A (en) 1990-12-20 1992-08-04 General Electric Company Slewable projection system with fiber-optic elements
US5098262A (en) 1990-12-28 1992-03-24 Abbott Laboratories Solution pumping system with compressible pump cassette
US5062819A (en) 1991-01-28 1991-11-05 Mallory Mitchell K Toy vehicle apparatus
JP2983658B2 (en) * 1991-02-14 1999-11-29 三洋電機株式会社 Vacuum cleaner
US5094311A (en) 1991-02-22 1992-03-10 Gmfanuc Robotics Corporation Limited mobility transporter
US5327952A (en) * 1991-03-08 1994-07-12 The Goodyear Tire & Rubber Company Pneumatic tire having improved wet traction
US5173881A (en) 1991-03-19 1992-12-22 Sindle Thomas J Vehicular proximity sensing system
US5105550A (en) 1991-03-25 1992-04-21 Wilson Sporting Goods Co. Apparatus for measuring golf clubs
US5258822A (en) 1991-04-11 1993-11-02 Honda Giken Kogyo Kabushiki Kaisha System for detecting the position of a moving body
US5400244A (en) 1991-06-25 1995-03-21 Kabushiki Kaisha Toshiba Running control system for mobile robot provided with multiple sensor information integration system
KR930005714B1 (en) 1991-06-25 1993-06-24 이헌조 Attratus and method for controlling speed of suction motor in vacuum cleaner
US5560065A (en) 1991-07-03 1996-10-01 Tymco, Inc. Broom assisted pick-up head
US5152202A (en) 1991-07-03 1992-10-06 The Ingersoll Milling Machine Company Turning machine with pivoted armature
DE4122280C2 (en) 1991-07-05 1994-08-18 Henkel Kgaa Mobile floor cleaning machine
DE69129407T2 (en) 1991-07-10 1998-11-19 Samsung Electronics Co Ltd Mobile monitoring device
KR930003937Y1 (en) 1991-08-14 1993-06-25 이헌조 Apparatus for detecting suction dirt for vacuum cleaner
US5442358A (en) 1991-08-16 1995-08-15 Kaman Aerospace Corporation Imaging lidar transmitter downlink for command guidance of underwater vehicle
US5227985A (en) 1991-08-19 1993-07-13 University Of Maryland Computer vision system for position monitoring in three dimensions using non-coplanar light sources attached to a monitored object
JP2738610B2 (en) 1991-09-07 1998-04-08 富士重工業株式会社 Travel control device of the self-propelled truck
JP2901112B2 (en) 1991-09-19 1999-06-07 矢崎総業株式会社 Vehicle environment monitoring device
DE4131667C2 (en) 1991-09-23 2002-07-18 Schlafhorst & Co W An apparatus for removing yarn residues
US5239720A (en) 1991-10-24 1993-08-31 Advance Machine Company Mobile surface cleaning machine
JP2555263Y2 (en) 1991-10-28 1997-11-19 日本電気ホームエレクトロニクス株式会社 Cleaning robot
WO1993009018A1 (en) 1991-11-05 1993-05-13 Seiko Epson Corporation Micro-robot
KR940006561B1 (en) 1991-12-30 1994-07-22 이헌조 Auto-drive sensor for vacuum cleaner
US5222786A (en) 1992-01-10 1993-06-29 Royal Appliance Mfg. Co. Wheel construction for vacuum cleaner
US5467273A (en) 1992-01-12 1995-11-14 State Of Israel, Ministry Of Defence, Rafael Armament Development Authority Large area movement robot
JP3076122B2 (en) 1992-01-13 2000-08-14 オリンパス光学工業株式会社 camera
EP0554978A3 (en) 1992-01-22 1994-03-09 Acushnet Co
DE4201596C2 (en) 1992-01-22 2001-07-05 Gerhard Kurz Floor nozzle for vacuum cleaners
US5502638A (en) 1992-02-10 1996-03-26 Honda Giken Kogyo Kabushiki Kaisha System for obstacle avoidance path planning for multiple-degree-of-freedom mechanism
US5276618A (en) * 1992-02-26 1994-01-04 The United States Of America As Represented By The Secretary Of The Navy Doorway transit navigational referencing system
US5568589A (en) 1992-03-09 1996-10-22 Hwang; Jin S. Self-propelled cleaning machine with fuzzy logic control
KR940004375B1 (en) 1992-03-25 1994-05-23 강진수 Drive system for automatic vacuum cleaner
JPH05285861A (en) 1992-04-07 1993-11-02 Fujita Corp Marking method for ceiling
US5277064A (en) * 1992-04-08 1994-01-11 General Motors Corporation Thick film accelerometer
FR2691093B1 (en) 1992-05-12 1996-06-14 Univ Joseph Fourier guide robot movements and control method thereof.
GB2267360B (en) 1992-05-22 1995-12-06 Octec Ltd Method and system for interacting with floating objects
DE4217093C1 (en) 1992-05-22 1993-07-01 Siemens Ag, 8000 Muenchen, De
US5206500A (en) 1992-05-28 1993-04-27 Cincinnati Microwave, Inc. Pulsed-laser detection with pulse stretcher and noise averaging
US6615434B1 (en) 1992-06-23 2003-09-09 The Kegel Company, Inc. Bowling lane cleaning machine and method
JPH064130A (en) 1992-06-23 1994-01-14 Sanyo Electric Co Ltd Cleaning robot
US5279672A (en) * 1992-06-29 1994-01-18 Windsor Industries, Inc. Automatic controlled cleaning machine
US5303448A (en) 1992-07-08 1994-04-19 Tennant Company Hopper and filter chamber for direct forward throw sweeper
US5331713A (en) 1992-07-13 1994-07-26 White Consolidated Industries, Inc. Floor scrubber with recycled cleaning solution
US5410479A (en) 1992-08-17 1995-04-25 Coker; William B. Ultrasonic furrow or crop row following sensor
JPH0662991A (en) 1992-08-21 1994-03-08 Yashima Denki Co Ltd Vacuum cleaner
US5613269A (en) 1992-10-26 1997-03-25 Miwa Science Laboratory Inc. Recirculating type cleaner
US5324948A (en) 1992-10-27 1994-06-28 The United States Of America As Represented By The United States Department Of Energy Autonomous mobile robot for radiologic surveys
US5548511A (en) 1992-10-29 1996-08-20 White Consolidated Industries, Inc. Method for controlling self-running cleaning apparatus
JPH06149350A (en) 1992-10-30 1994-05-27 Johnson Kk Guidance system for self-traveling car
US5319828A (en) 1992-11-04 1994-06-14 Tennant Company Low profile scrubber
US5369838A (en) 1992-11-16 1994-12-06 Advance Machine Company Automatic floor scrubber
US5261139A (en) 1992-11-23 1993-11-16 Lewis Steven D Raised baseboard brush for powered floor sweeper
GB9324477D0 (en) 1992-12-19 1994-01-12 Fedag Vacuum cleaning tool with an electrically driven brush roller
US5284452A (en) * 1993-01-15 1994-02-08 Atlantic Richfield Company Mooring buoy with hawser tension indicator system
US5491670A (en) * 1993-01-21 1996-02-13 Weber; T. Jerome System and method for sonic positioning
US5315227A (en) 1993-01-29 1994-05-24 Pierson Mark V Solar recharge station for electric vehicles
US5310379A (en) 1993-02-03 1994-05-10 Mattel, Inc. Multiple configuration toy vehicle
DE9303254U1 (en) 1993-03-05 1993-09-30 Raimondi Srl Machine for washing tiled surfaces
US5451135A (en) 1993-04-02 1995-09-19 Carnegie Mellon University Collapsible mobile vehicle
US5345649A (en) 1993-04-21 1994-09-13 Whitlow William T Fan brake for textile cleaning machine
US5352901A (en) 1993-04-26 1994-10-04 Cummins Electronics Company, Inc. Forward and back scattering loss compensated smoke detector
US5435405A (en) 1993-05-14 1995-07-25 Carnegie Mellon University Reconfigurable mobile vehicle with magnetic tracks
US5363935A (en) 1993-05-14 1994-11-15 Carnegie Mellon University Reconfigurable mobile vehicle with magnetic tracks
US5440216A (en) 1993-06-08 1995-08-08 Samsung Electronics Co., Ltd. Robot cleaner
US5460124A (en) 1993-07-15 1995-10-24 Perimeter Technologies Incorporated Receiver for an electronic animal confinement system
US5497529A (en) 1993-07-20 1996-03-12 Boesi; Anna M. Electrical apparatus for cleaning surfaces by suction in dwelling premises
FR2708188A1 (en) 1993-07-28 1995-02-03 Philips Laboratoire Electroniq Vacuum cleaner with soil detection means and adjusting the engine power based on the detected soil.
JPH07152433A (en) 1993-08-07 1995-06-16 Samsung Electron Co Ltd Vacuum cleaner and control method therefor
US5586063A (en) 1993-09-01 1996-12-17 Hardin; Larry C. Optical range and speed detection system
CA2128676C (en) 1993-09-08 1997-12-23 John D. Sotack Capacitive sensor
KR0161031B1 (en) 1993-09-09 1998-12-15 김광호 Position error correction device of robot
KR100197676B1 (en) 1993-09-27 1999-06-15 윤종용 Robot cleaner
JP3319093B2 (en) 1993-11-08 2002-08-26 松下電器産業株式会社 Mobile work robot
GB9323316D0 (en) 1993-11-11 1994-01-05 Crowe Gordon M Motorized carrier
DE4338841C2 (en) 1993-11-13 1999-08-05 Axel Dickmann lamp
GB2284957B (en) 1993-12-14 1998-02-18 Gec Marconi Avionics Holdings Optical systems for the remote tracking of the position and/or orientation of an object
JP2594880B2 (en) 1993-12-29 1997-03-26 株式会社友清白蟻 Autonomous type intelligence work robot
US5511147A (en) 1994-01-12 1996-04-23 Uti Corporation Graphical interface for robot
BE1008777A6 (en) * 1994-02-11 1996-08-06 Solar And Robotics Sa Power system of mobile autonomous robots.
US5553349A (en) 1994-02-21 1996-09-10 Aktiebolaget Electrolux Vacuum cleaner nozzle
US5608306A (en) 1994-03-15 1997-03-04 Ericsson Inc. Rechargeable battery pack with identification circuit, real time clock and authentication capability
JP3201903B2 (en) 1994-03-18 2001-08-27 富士通株式会社 Semiconductor logic circuit and a semiconductor integrated circuit device using the same
JPH07262025A (en) 1994-03-18 1995-10-13 Fujitsu Ltd Execution control system
JP3530954B2 (en) 1994-03-24 2004-05-24 吉寛 木内 Far-infrared radiation sterilization device
WO1995026512A1 (en) 1994-03-29 1995-10-05 Aktiebolaget Electrolux Method and device for sensing of obstacles for an autonomous device
US5646494A (en) 1994-03-29 1997-07-08 Samsung Electronics Co., Ltd. Charge induction apparatus of robot cleaner and method thereof
JPH07265240A (en) 1994-03-31 1995-10-17 Hookii:Kk Wall side cleaning body for floor cleaner
KR970000582B1 (en) 1994-03-31 1997-01-14 김광호 Method for controlling driving of a robot cleaner
JP3293314B2 (en) 1994-04-14 2002-06-17 ミノルタ株式会社 Cleaning Robot
DE4414683A1 (en) 1994-04-15 1995-10-19 Vorwerk Co Interholding cleaner
US5455982A (en) 1994-04-22 1995-10-10 Advance Machine Company Hard and soft floor surface cleaning apparatus
US5802665A (en) 1994-04-25 1998-09-08 Widsor Industries, Inc. Floor cleaning apparatus with two brooms
US5485653A (en) 1994-04-25 1996-01-23 Windsor Industries, Inc. Floor cleaning apparatus
EP0759157B1 (en) 1994-05-10 1999-07-07 Heinrich Iglseder Method of detecting particles in a two-phase stream, use of such method and a vacuum cleaner
US5507067A (en) 1994-05-12 1996-04-16 Newtronics Pty Ltd. Electronic vacuum cleaner control system
JPH07319542A (en) 1994-05-30 1995-12-08 Minolta Co Ltd Self-traveling work wagon
GB2290143B (en) 1994-06-06 1998-03-18 Electrolux Ab Improved method for localization of beacons for an autonomous device
US5735959A (en) 1994-06-15 1998-04-07 Minolta Co, Ltd. Apparatus spreading fluid on floor while moving
US5636402A (en) 1994-06-15 1997-06-10 Minolta Co., Ltd. Apparatus spreading fluid on floor while moving
JP3346513B2 (en) 1994-07-01 2002-11-18 ミノルタ株式会社 Map storage method and route generation method that uses the map
BE1008470A3 (en) 1994-07-04 1996-05-07 Colens Andre Device and automatic system and equipment dedusting sol y adapted.
JPH0822322A (en) 1994-07-07 1996-01-23 Johnson Kk Method and device for controlling floor surface cleaning car
JP2569279B2 (en) 1994-08-01 1997-01-08 コナミ株式会社 Non-contact type position detecting device of the mobile
CA2137706C (en) 1994-12-09 2001-03-20 Murray Evans Cutting mechanism
US5551525A (en) 1994-08-19 1996-09-03 Vanderbilt University Climber robot
JP3296105B2 (en) 1994-08-26 2002-06-24 ミノルタ株式会社 Autonomous mobile robot
US5454129A (en) 1994-09-01 1995-10-03 Kell; Richard T. Self-powered pool vacuum with remote controlled capabilities
JP3197758B2 (en) 1994-09-13 2001-08-13 日本電信電話株式会社 Optical coupling device and manufacturing method thereof
JP3188116B2 (en) 1994-09-26 2001-07-16 日本輸送機株式会社 Self-propelled vacuum cleaner
US6188643B1 (en) * 1994-10-13 2001-02-13 Schlumberger Technology Corporation Method and apparatus for inspecting well bore casing
US5498948A (en) 1994-10-14 1996-03-12 Delco Electornics Self-aligning inductive charger
US5546631A (en) 1994-10-31 1996-08-20 Chambon; Michael D. Waterless container cleaner monitoring system
GB9422911D0 (en) 1994-11-14 1995-01-04 Moonstone Technology Ltd Capacitive touch detectors
US5505072A (en) 1994-11-15 1996-04-09 Tekscan, Inc. Scanning circuit for pressure responsive array
US5560077A (en) 1994-11-25 1996-10-01 Crotchett; Diane L. Vacuum dustpan apparatus
GB9500943D0 (en) 1994-12-01 1995-03-08 Popovich Milan M Optical position sensing system
US6220865B1 (en) 1996-01-22 2001-04-24 Vincent J. Macri Instruction for groups of users interactively controlling groups of images to make idiosyncratic, simulated, physical movements
US5710506A (en) * 1995-02-07 1998-01-20 Benchmarq Microelectronics, Inc. Lead acid charger
KR100384194B1 (en) 1995-03-22 2003-08-21 혼다 기켄 고교 가부시키가이샤 Desiccant wall walking device
US5634237A (en) 1995-03-29 1997-06-03 Paranjpe; Ajit P. Self-guided, self-propelled, convertible cleaning apparatus
EP0735195B1 (en) 1995-03-31 2000-06-28 DULEVO INTERNATIONAL S.p.A. Sucking and filtering vehicle for dust and trash collecting
US5947225A (en) 1995-04-14 1999-09-07 Minolta Co., Ltd. Automatic vehicle
JP3887678B2 (en) * 1995-04-21 2007-02-28 フォルベルク・ウント・ツェーオー、インターホールディング・ゲーエムベーハー Attachment of wet cleaning vacuum cleaner of surface
GB2300082B (en) 1995-04-21 1999-09-22 British Aerospace Altitude measuring methods
US5537711A (en) 1995-05-05 1996-07-23 Tseng; Yu-Che Electric board cleaner
US5634239A (en) 1995-05-16 1997-06-03 Aktiebolaget Electrolux Vacuum cleaner nozzle
EP0829040B1 (en) 1995-05-30 2002-04-03 Friendly Robotics Limited Navigation method and system
US5655658A (en) 1995-05-31 1997-08-12 Eastman Kodak Company Cassette container having effective centering capability
US5781697A (en) 1995-06-02 1998-07-14 Samsung Electronics Co., Ltd. Method and apparatus for automatic running control of a robot
US5608944A (en) 1995-06-05 1997-03-11 The Hoover Company Vacuum cleaner with dirt detection
US5935333A (en) 1995-06-07 1999-08-10 The Kegel Company Variable speed bowling lane maintenance machine
JPH08335112A (en) 1995-06-08 1996-12-17 Minolta Co Ltd Mobile working robot system
JP2640736B2 (en) 1995-07-13 1997-08-13 株式会社エイシン技研 Cleaning machines and bowling lane maintenance machine
US5555587A (en) 1995-07-20 1996-09-17 The Scott Fetzer Company Floor mopping machine
US5764888A (en) 1995-07-20 1998-06-09 Dallas Semiconductor Corporation Electronic micro identification circuit that is inherently bonded to someone or something
JPH0947413A (en) 1995-08-08 1997-02-18 Minolta Co Ltd Cleaning robot
EP0760460B1 (en) 1995-08-28 2002-07-03 Matsushita Electric Works, Ltd. Optical displacement measuring system using a triangulation
JP4014662B2 (en) 1995-09-18 2007-11-28 ファナック株式会社 Robot teaching pendant
JP3152622B2 (en) 1995-09-19 2001-04-03 光雄 藤井 Wiper cleaning method and apparatus
US5819008A (en) 1995-10-18 1998-10-06 Rikagaku Kenkyusho Mobile robot sensor system
GB2348029B (en) 1995-10-20 2001-01-03 Baker Hughes Inc Communication in a wellbore utilizing acoustic signals
WO1997015224A1 (en) 1995-10-27 1997-05-01 Aktiebolaget Electrolux Vacuum cleaner nozzle
KR0133745B1 (en) 1995-10-31 1998-04-24 배순훈 Dust meter device of a vacuum cleaner
US6041472A (en) 1995-11-06 2000-03-28 Bissell Homecare, Inc. Upright water extraction cleaning machine
US5867861A (en) * 1995-11-13 1999-02-09 Kasen; Timothy E. Upright water extraction cleaning machine with two suction nozzles
US5777596A (en) 1995-11-13 1998-07-07 Symbios, Inc. Touch sensitive flat panel display
US5996167A (en) 1995-11-16 1999-12-07 3M Innovative Properties Company Surface treating articles and method of making same
JP3025348U (en) 1995-11-30 1996-06-11 株式会社トミー Traveling body
JPH09160644A (en) 1995-12-06 1997-06-20 Fujitsu General Ltd Control method for floor cleaning robot
US6049620A (en) 1995-12-15 2000-04-11 Veridicom, Inc. Capacitive fingerprint sensor with adjustable gain
US5710700A (en) * 1995-12-18 1998-01-20 International Business Machines Corporation Optimizing functional operation in manufacturing control
RU2121288C1 (en) 1995-12-19 1998-11-10 Квангджу Электроникс Ко., Лтд Vacuum cleaner
JPH09179625A (en) 1995-12-26 1997-07-11 Hitachi Electric Syst:Kk Method for controlling traveling of autonomous traveling vehicle and controller therefor
JPH09179100A (en) 1995-12-27 1997-07-11 Sharp Corp Picture display device
US5793900A (en) 1995-12-29 1998-08-11 Stanford University Generating categorical depth maps using passive defocus sensing
US5989700A (en) 1996-01-05 1999-11-23 Tekscan Incorporated Pressure sensitive ink means, and methods of use
JPH09185410A (en) 1996-01-08 1997-07-15 Hitachi Electric Syst:Kk Method and device for controlling traveling of autonomous traveling vehicle
US5784755A (en) 1996-01-18 1998-07-28 White Consolidated Industries, Inc. Wet extractor system
US5611106A (en) 1996-01-19 1997-03-18 Castex Incorporated Carpet maintainer
JP3660042B2 (en) 1996-02-01 2005-06-15 富士重工業株式会社 Method of controlling the cleaning robot
FR2744810A1 (en) 1996-02-14 1997-08-14 Sodern Solar viewfinder slot
DE19605573C2 (en) 1996-02-15 2000-08-24 Eurocopter Deutschland Three-axis joystick drehpositionierbarer
DE19605780A1 (en) 1996-02-16 1997-08-21 Branofilter Gmbh Detection device for filter bags in vacuum cleaners
US5828770A (en) 1996-02-20 1998-10-27 Northern Digital Inc. System for determining the spatial position and angular orientation of an object
US5659918A (en) 1996-02-23 1997-08-26 Breuer Electric Mfg. Co. Vacuum cleaner and method
EP0847549B1 (en) 1996-03-06 1999-09-22 GMD-Forschungszentrum Informationstechnik GmbH Autonomous mobile robot system for sensor-based and map-based navigation in pipe networks
JPH09244730A (en) 1996-03-11 1997-09-19 Komatsu Ltd Robot system and controller for robot
BE1013948A3 (en) 1996-03-26 2003-01-14 Egemin Naanloze Vennootschap MEASURING SYSTEM FOR POSITION OF THE KEYS OF A VEHICLE AND ABOVE sensing device.
JPH09263140A (en) 1996-03-27 1997-10-07 Minolta Co Ltd Unmanned service car
US5735017A (en) 1996-03-29 1998-04-07 Bissell Inc. Compact wet/dry vacuum cleaner with flexible bladder
US5732401A (en) 1996-03-29 1998-03-24 Intellitecs International Ltd. Activity based cost tracking systems
US5831719A (en) 1996-04-12 1998-11-03 Holometrics, Inc. Laser scanning system
US5781960A (en) 1996-04-25 1998-07-21 Aktiebolaget Electrolux Nozzle arrangement for a self-guiding vacuum cleaner
ES2165054T3 (en) 1996-04-30 2002-03-01 Electrolux Ab System and device for an automatic orientation.
US5935179A (en) 1996-04-30 1999-08-10 Aktiebolaget Electrolux System and device for a self orienting device
WO1997040734A1 (en) 1996-04-30 1997-11-06 Aktiebolaget Electrolux (Publ) Autonomous device
DE19617986B4 (en) 1996-05-04 2004-02-26 Ing. Haaga Werkzeugbau Kg sweeper
US5742975A (en) 1996-05-06 1998-04-28 Windsor Industries, Inc. Articulated floor scrubber
US6160479A (en) 1996-05-07 2000-12-12 Besam Ab Method for the determination of the distance and the angular position of an object
JP3343027B2 (en) 1996-05-17 2002-11-11 アマノ株式会社 Squeegee for floor cleaning machines
US5831597A (en) 1996-05-24 1998-11-03 Tanisys Technology, Inc. Computer input device for use in conjunction with a mouse input device
JP3493539B2 (en) 1996-06-03 2004-02-03 ミノルタ株式会社 Running and working robot
JPH09315061A (en) 1996-06-03 1997-12-09 Minolta Co Ltd Ic card and ic card-mounting apparatus
JPH09324875A (en) * 1996-06-03 1997-12-16 Minolta Co Ltd Tank
US5983448A (en) 1996-06-07 1999-11-16 Royal Appliance Mfg. Co. Cordless wet mop and vacuum assembly
JP3581911B2 (en) 1996-06-07 2004-10-27 コニカミノルタホールディングス株式会社 Moving vehicles
US6101671A (en) 1996-06-07 2000-08-15 Royal Appliance Mfg. Co. Wet mop and vacuum assembly
US6065182A (en) 1996-06-07 2000-05-23 Royal Appliance Mfg. Co. Cordless wet mop and vacuum assembly
US5709007A (en) * 1996-06-10 1998-01-20 Chiang; Wayne Remote control vacuum cleaner
US5767960A (en) 1996-06-14 1998-06-16 Ascension Technology Corporation Optical 6D measurement system with three fan-shaped beams rotating around one axis
CA2255728C (en) * 1996-06-26 2004-03-30 Henry Marcussen Extractor with twin, counterrotating agitators
US6052821A (en) 1996-06-26 2000-04-18 U.S. Philips Corporation Trellis coded QAM using rate compatible, punctured, convolutional codes
US5812267A (en) 1996-07-10 1998-09-22 The United States Of America As Represented By The Secretary Of The Navy Optically based position location system for an autonomous guided vehicle
US6142252A (en) 1996-07-11 2000-11-07 Minolta Co., Ltd. Autonomous vehicle that runs while recognizing work area configuration, and method of selecting route
JP3395874B2 (en) 1996-08-12 2003-04-14 ミノルタ株式会社 Moving vehicles
US5926909A (en) 1996-08-28 1999-07-27 Mcgee; Daniel Remote control vacuum cleaner and charging system
US5756904A (en) 1996-08-30 1998-05-26 Tekscan, Inc. Pressure responsive sensor having controlled scanning speed
JPH10105236A (en) 1996-09-30 1998-04-24 Minolta Co Ltd Positioning device for traveling object and its method
US5829095A (en) 1996-10-17 1998-11-03 Nilfisk-Advance, Inc. Floor surface cleaning machine
DE19643465C2 (en) 1996-10-22 1999-08-05 Bosch Gmbh Robert A control device for an optical sensor, in particular a rain sensor
JPH10117973A (en) 1996-10-23 1998-05-12 Minolta Co Ltd Autonomous moving vehicle
JPH10118963A (en) 1996-10-23 1998-05-12 Minolta Co Ltd Autonomous mobil vehicle
DE19644570C2 (en) 1996-10-26 1999-11-18 Kaercher Gmbh & Co Alfred Mobile floor cleaning device
US5815884A (en) 1996-11-27 1998-10-06 Yashima Electric Co., Ltd. Dust indication system for vacuum cleaner
DE69607629T2 (en) 1996-11-29 2000-10-19 Yashima Electric Co vacuum cleaner
JP3525658B2 (en) 1996-12-12 2004-05-10 松下電器産業株式会社 Air purifier operation controller
US5940346A (en) 1996-12-13 1999-08-17 Arizona Board Of Regents Modular robotic platform with acoustic navigation system
US5974348A (en) 1996-12-13 1999-10-26 Rocks; James K. System and method for performing mobile robotic work operations
JPH10177414A (en) 1996-12-16 1998-06-30 Matsushita Electric Ind Co Ltd Device for recognizing traveling state by ceiling picture
US5987696A (en) 1996-12-24 1999-11-23 Wang; Kevin W. Carpet cleaning machine
US6146278A (en) 1997-01-10 2000-11-14 Konami Co., Ltd. Shooting video game machine
WO1998033103A1 (en) 1997-01-22 1998-07-30 Siemens Aktiengesellschaft Method and device for docking an autonomous mobile unit
US6076226A (en) 1997-01-27 2000-06-20 Robert J. Schaap Controlled self operated vacuum cleaning system
JP3731021B2 (en) 1997-01-31 2006-01-05 株式会社トプコン Position detection surveying instrument
US5819367A (en) 1997-02-25 1998-10-13 Yashima Electric Co., Ltd. Vacuum cleaner with optical sensor
JPH10240343A (en) 1997-02-27 1998-09-11 Minolta Co Ltd Autonomously traveling vehicle
JPH10240342A (en) 1997-02-28 1998-09-11 Minolta Co Ltd Autonomous traveling vehicle
DE19708955A1 (en) 1997-03-05 1998-09-10 Bosch Siemens Hausgeraete multifunctional vacuum cleaning device
US5860707A (en) 1997-03-13 1999-01-19 Rollerblade, Inc. In-line skate wheel
ES2205458T3 (en) 1997-03-18 2004-05-01 Solar And Robotics S.A. Improvements for a robotic mower.
WO1998041822A1 (en) 1997-03-20 1998-09-24 Crotzer David R Dust sensor apparatus
US5767437A (en) 1997-03-20 1998-06-16 Rogers; Donald L. Digital remote pyrotactic firing mechanism
JPH10260727A (en) 1997-03-21 1998-09-29 Minolta Co Ltd Automatic traveling working vehicle
US6587573B1 (en) 2000-03-20 2003-07-01 Gentex Corporation System for controlling exterior vehicle lights
JPH10295595A (en) 1997-04-23 1998-11-10 Minolta Co Ltd Autonomously moving work wagon
US5987383C1 (en) 1997-04-28 2006-06-13 Trimble Navigation Ltd Form line following guidance system
US6557104B2 (en) 1997-05-02 2003-04-29 Phoenix Technologies Ltd. Method and apparatus for secure processing of cryptographic keys
US6108031A (en) 1997-05-08 2000-08-22 Kaman Sciences Corporation Virtual reality teleoperated remote control vehicle
KR200155821Y1 (en) 1997-05-12 1999-10-01 최진호 Remote controller of vacuum cleaner
JPH10314088A (en) 1997-05-15 1998-12-02 Fuji Heavy Ind Ltd Self-advancing type cleaner
WO1998053456A1 (en) 1997-05-19 1998-11-26 Creator Ltd. Apparatus and methods for controlling household appliances
US6070290A (en) 1997-05-27 2000-06-06 Schwarze Industries, Inc. High maneuverability riding turf sweeper and surface cleaning apparatus
DE69831181D1 (en) 1997-05-30 2005-09-15 British Broadcasting Corp location
GB2326353B (en) 1997-06-20 2001-02-28 Wong T K Ass Ltd Toy
JPH1115941A (en) 1997-06-24 1999-01-22 Minolta Co Ltd Ic card, and ic card system including the same
US6009358A (en) 1997-06-25 1999-12-28 Thomas G. Xydis Programmable lawn mower
US6032542A (en) 1997-07-07 2000-03-07 Tekscan, Inc. Prepressured force/pressure sensor and method for the fabrication thereof
US6192548B1 (en) * 1997-07-09 2001-02-27 Bissell Homecare, Inc. Upright extraction cleaning machine with flow rate indicator
US6131237A (en) 1997-07-09 2000-10-17 Bissell Homecare, Inc. Upright extraction cleaning machine
US6438793B1 (en) 1997-07-09 2002-08-27 Bissell Homecare, Inc. Upright extraction cleaning machine
US6167587B1 (en) 1997-07-09 2001-01-02 Bissell Homecare, Inc. Upright extraction cleaning machine
US5905209A (en) 1997-07-22 1999-05-18 Tekscan, Inc. Output circuit for pressure sensor
WO1999005580A3 (en) 1997-07-23 1999-04-15 Horst Juergen Duschek Method for controlling an unmanned transport vehicle and unmanned transport vehicle system therefor
US5950408A (en) 1997-07-25 1999-09-14 Mtd Products Inc Bag-full indicator mechanism
US5821730A (en) 1997-08-18 1998-10-13 International Components Corp. Low cost battery sensing technique
US6226830B1 (en) 1997-08-20 2001-05-08 Philips Electronics North America Corp. Vacuum cleaner with obstacle avoidance
US5998953A (en) 1997-08-22 1999-12-07 Minolta Co., Ltd. Control apparatus of mobile that applies fluid on floor
CN1155326C (en) 1997-08-25 2004-06-30 皇家菲利浦电子有限公司 Electrical surface treatment device with an acoustic surface type detector
JPH1165655A (en) 1997-08-26 1999-03-09 Minolta Co Ltd Controller for mobile object
CN1212859A (en) 1997-08-29 1999-04-07 三洋电机株式会社 Suction head of electric dust collector
DE19738163A1 (en) 1997-09-01 1999-03-11 Siemens Ag A process for Andockpositionierung an autonomous mobile unit using a guide beam
US6199181B1 (en) 1997-09-09 2001-03-06 Perfecto Technologies Ltd. Method and system for maintaining restricted operating environments for application programs or operating systems
US6023814A (en) * 1997-09-15 2000-02-15 Imamura; Nobuo Vacuum cleaner
DE29824544U1 (en) 1997-09-19 2001-08-09 Electrolux Ab An electronic bordering system
GB2329513B (en) 1997-09-19 2001-04-11 Samsung Display Devices Co Ltd A method of preparing an electrode for lithium based secondary cell
WO1999016078A1 (en) 1997-09-19 1999-04-01 Hitachi, Ltd. Synchronous integrated circuit device
US5933102A (en) 1997-09-24 1999-08-03 Tanisys Technology, Inc. Capacitive sensitive switch method and system
JPH11102220A (en) 1997-09-26 1999-04-13 Minolta Co Ltd Controller for moving body
JPH11102219A (en) * 1997-09-26 1999-04-13 Minolta Co Ltd Controller for moving body
US6076026A (en) 1997-09-30 2000-06-13 Motorola, Inc. Method and device for vehicle control events data recording and securing
US20010032278A1 (en) 1997-10-07 2001-10-18 Brown Stephen J. Remote generation and distribution of command programs for programmable devices
DE69812673D1 (en) 1997-10-17 2003-04-30 Ndc Automation Ab Saeroe Method and apparatus for allocation of the anonymous reflectors to the detected angular position
US5974365A (en) 1997-10-23 1999-10-26 The United States Of America As Represented By The Secretary Of The Army System for measuring the location and orientation of an object
DE19747318C1 (en) 1997-10-27 1999-05-27 Kaercher Gmbh & Co Alfred cleaner
FR2770672B1 (en) 1997-11-04 2000-01-21 Inst Nat Rech Inf Automat Method and locating and guiding device of a mobile camera fitted with a linear
US5943730A (en) 1997-11-24 1999-08-31 Tennant Company Scrubber vac-fan seal
US6532404B2 (en) 1997-11-27 2003-03-11 Colens Andre Mobile robots and their control system
US6389329B1 (en) 1997-11-27 2002-05-14 Andre Colens Mobile robots and their control system
GB2331919B (en) 1997-12-05 2002-05-08 Bissell Inc Handheld extraction cleaner
GB9726104D0 (en) 1997-12-10 1998-02-11 Nec Technologies Uk Ltd Coulometric battery state of charge metering
JPH11175149A (en) 1997-12-10 1999-07-02 Minolta Co Ltd Autonomous traveling vehicle
JPH11174145A (en) 1997-12-11 1999-07-02 Minolta Co Ltd Ultrasonic range finding sensor and autonomous driving vehicle
US6055042A (en) 1997-12-16 2000-04-25 Caterpillar Inc. Method and apparatus for detecting obstacles using multiple sensors for range selective detection
JPH11178764A (en) 1997-12-22 1999-07-06 Honda Motor Co Ltd Traveling robot
JP3426487B2 (en) 1997-12-22 2003-07-14 本田技研工業株式会社 Cleaning robot
WO1999038237A1 (en) 1998-01-08 1999-07-29 Aktiebolaget Electrolux Docking system for a self-propelled working tool
WO1999038056A1 (en) 1998-01-08 1999-07-29 Aktiebolaget Electrolux Electronic search system
US6003196A (en) 1998-01-09 1999-12-21 Royal Appliance Mfg. Co. Upright vacuum cleaner with cyclonic airflow
US5967747A (en) 1998-01-20 1999-10-19 Tennant Company Low noise fan
US5984880A (en) 1998-01-20 1999-11-16 Lander; Ralph H Tactile feedback controlled by various medium
US6099091A (en) 1998-01-20 2000-08-08 Letro Products, Inc. Traction enhanced wheel apparatus
JP3479212B2 (en) 1998-01-21 2003-12-15 本田技研工業株式会社 The method and apparatus of the self-propelled robot
CA2251295C (en) 1998-01-27 2002-08-20 Sharp Kabushiki Kaisha Electric vacuum cleaner
US6030464A (en) * 1998-01-28 2000-02-29 Azevedo; Steven Method for diagnosing, cleaning and preserving carpeting and other fabrics
JPH11213157A (en) 1998-01-29 1999-08-06 Minolta Co Ltd Camera mounted mobile object
DE19804195A1 (en) 1998-02-03 1999-08-05 Siemens Ag Path planning process for a mobile unit to face machining
US6272936B1 (en) 1998-02-20 2001-08-14 Tekscan, Inc Pressure sensor
WO1999043250A1 (en) 1998-02-26 1999-09-02 Aktiebolaget Electrolux Vacuum cleaner nozzle
US6036572A (en) 1998-03-04 2000-03-14 Sze; Chau-King Drive for toy with suction cup feet
US6026539A (en) * 1998-03-04 2000-02-22 Bissell Homecare, Inc. Upright vacuum cleaner with full bag and clogged filter indicators thereon
ES2163907T3 (en) 1998-03-12 2002-02-01 Cavanna Spa A method for controlling the operation of a machine for the treatment of articles, eg for packaging food products and machines for the purpose.
JPH11282533A (en) 1998-03-26 1999-10-15 Sharp Corp Mobile robot system
US6263989B1 (en) 1998-03-27 2001-07-24 Irobot Corporation Robotic platform
JP3479215B2 (en) 1998-03-27 2003-12-15 本田技研工業株式会社 Self-propelled robot control method and apparatus according to the mark detection
KR100384980B1 (en) 1998-04-03 2003-06-02 마츠시타 덴끼 산교 가부시키가이샤 Rotational brush device and electric instrument using same
US6023813A (en) * 1998-04-07 2000-02-15 Spectrum Industrial Products, Inc. Powered floor scrubber and buffer
US6154279A (en) 1998-04-09 2000-11-28 John W. Newman Method and apparatus for determining shapes of countersunk holes
US6041471A (en) 1998-04-09 2000-03-28 Madvac International Inc. Mobile walk-behind sweeper
JPH11295412A (en) 1998-04-09 1999-10-29 Minolta Co Ltd Apparatus for recognizing position of mobile
EP1086383A4 (en) 1998-04-15 2005-04-20 Commw Scient Ind Res Org Method of tracking and sensing position of objects
US6233504B1 (en) 1998-04-16 2001-05-15 California Institute Of Technology Tool actuation and force feedback on robot-assisted microsurgery system
DE19820628C1 (en) 1998-05-08 1999-09-23 Kaercher Gmbh & Co Alfred Roller mounting or carpet sweeper
DE69941117D1 (en) 1998-05-11 2009-08-27 F Robotics Acquisitions Ltd Area cover by means of an autonomous robot
JP3895464B2 (en) 1998-05-11 2007-03-22 株式会社東海理化電機製作所 Data carrier system
EP2416198B1 (en) 1998-05-25 2013-05-01 Panasonic Corporation Range finder device and camera
JP3597384B2 (en) 1998-06-08 2004-12-08 シャープ株式会社 Vacuum cleaner
US6941199B1 (en) 1998-07-20 2005-09-06 The Procter & Gamble Company Robotic system
WO2000004430A8 (en) 1998-07-20 2000-04-20 Ian Bottomley Robotic system
JP2000047728A (en) 1998-07-28 2000-02-18 Denso Corp Electric charging controller in moving robot system
US6108859A (en) 1998-07-29 2000-08-29 Alto U. S. Inc. High efficiency squeegee
US6112143A (en) 1998-08-06 2000-08-29 Caterpillar Inc. Method and apparatus for establishing a perimeter defining an area to be traversed by a mobile machine
EP1105782A2 (en) 1998-08-10 2001-06-13 Siemens Aktiengesellschaft Method and device for determining a path around a defined reference position
JP2000056831A (en) 1998-08-12 2000-02-25 Minolta Co Ltd Moving travel vehicle
US6088020A (en) 1998-08-12 2000-07-11 Mitsubishi Electric Information Technology Center America, Inc. (Ita) Haptic device
JP2000056006A (en) 1998-08-14 2000-02-25 Minolta Co Ltd Position recognizing device for mobile
US6491127B1 (en) 1998-08-14 2002-12-10 3Com Corporation Powered caster wheel module for use on omnidirectional drive systems
JP3478476B2 (en) 1998-08-18 2003-12-15 シャープ株式会社 Cleaning robot
JP2000066722A (en) 1998-08-19 2000-03-03 Minolta Co Ltd Autonomously traveling vehicle and rotation angle detection method
JP2000075925A (en) 1998-08-28 2000-03-14 Minolta Co Ltd Autonomous traveling vehicle
US6216307B1 (en) 1998-09-25 2001-04-17 Cma Manufacturing Co. Hand held cleaning device
US20020104963A1 (en) 1998-09-26 2002-08-08 Vladimir Mancevski Multidimensional sensing system for atomic force microscopy
US20030233870A1 (en) 2001-07-18 2003-12-25 Xidex Corporation Multidimensional sensing system for atomic force microscopy
JP2000102499A (en) 1998-09-30 2000-04-11 Kankyo Co Ltd Vacuum cleaner with rotary brush
US6108269A (en) 1998-10-01 2000-08-22 Garmin Corporation Method for elimination of passive noise interference in sonar
CA2251243C (en) 1998-10-21 2006-12-19 Robert Dworkowski Distance tracking control system for single pass topographical mapping
DE19849978C2 (en) 1998-10-29 2001-02-08 Erwin Prasler A self-propelled cleaning device
EP1155787B1 (en) 1998-11-30 2016-10-05 Sony Corporation Robot device and control method thereof
JP3980205B2 (en) 1998-12-17 2007-09-26 コニカミノルタホールディングス株式会社 Working robot
GB2344745B (en) 1998-12-18 2002-06-05 Notetry Ltd Vacuum cleaner
GB2344751B (en) 1998-12-18 2002-01-09 Notetry Ltd Vacuum cleaner
GB2344750B (en) 1998-12-18 2002-06-26 Notetry Ltd Vacuum cleaner
GB2344747B (en) 1998-12-18 2002-05-29 Notetry Ltd Autonomous vacuum cleaner
GB9827771D0 (en) 1998-12-18 1999-02-10 Notetry Ltd Light detection apparatus
GB9827779D0 (en) 1998-12-18 1999-02-10 Notetry Ltd Improvements in or relating to appliances
US6101670A (en) 1998-12-31 2000-08-15 Song; Young-So Dust collection tester for a vacuum cleaner
US6154917A (en) 1999-01-08 2000-12-05 Royal Appliance Mfg. Co. Carpet extractor housing
US6238451B1 (en) 1999-01-08 2001-05-29 Fantom Technologies Inc. Vacuum cleaner
DE19900484A1 (en) 1999-01-08 2000-08-10 Wap Reinigungssysteme Measuring system for residual dust monitoring for safety vacuum cleaner
US6282526B1 (en) 1999-01-20 2001-08-28 The United States Of America As Represented By The Secretary Of The Navy Fuzzy logic based system and method for information processing with uncertain input data
US6167332A (en) 1999-01-28 2000-12-26 International Business Machines Corporation Method and apparatus suitable for optimizing an operation of a self-guided vehicle
US6124694A (en) 1999-03-18 2000-09-26 Bancroft; Allen J. Wide area navigation for a robot scrubber
US6611738B2 (en) 1999-07-12 2003-08-26 Bryan J. Ruffner Multifunctional mobile appliance
JP3513419B2 (en) 1999-03-19 2004-03-31 キヤノン株式会社 Coordinate input apparatus and its control method, a computer-readable memory
JP2000275321A (en) 1999-03-25 2000-10-06 Aiseko:Kk Method and system for measuring position coordinate of traveling object
US6688951B2 (en) 1999-03-26 2004-02-10 Fuji Photo Film Co., Ltd. Thermal head lapping apparatus
JP4198262B2 (en) 1999-03-29 2008-12-17 富士重工業株式会社 Position adjusting mechanism of the dust suction device in the floor cleaning robot
WO2000067960A1 (en) 1999-05-10 2000-11-16 Sony Corporation Toboy device and method for controlling the same
US7707082B1 (en) 1999-05-25 2010-04-27 Silverbrook Research Pty Ltd Method and system for bill management
US6202243B1 (en) 1999-05-26 2001-03-20 Tennant Company Surface cleaning machine with multiple control positions
GB9912472D0 (en) 1999-05-28 1999-07-28 Notetry Ltd Indicator
US6261379B1 (en) 1999-06-01 2001-07-17 Fantom Technologies Inc. Floating agitator housing for a vacuum cleaner head
JP3803291B2 (en) 1999-06-08 2006-08-02 ジョンソンディバーシー・インコーポレーテッド Floor cleaning device
JP3598881B2 (en) 1999-06-09 2004-12-08 株式会社豊田自動織機 Cleaning Robot
US6446302B1 (en) 1999-06-14 2002-09-10 Bissell Homecare, Inc. Extraction cleaning machine with cleaning control
EP1191982B1 (en) 1999-06-17 2004-06-02 Solar & Robotics S.A. Device for automatically picking up objects
WO2001000079A3 (en) 1999-06-30 2001-10-04 Patrick Enzler Riding floor scrubber
JP4165965B2 (en) 1999-07-09 2008-10-15 フィグラ株式会社 Autonomous work vehicle
GB9917232D0 (en) 1999-07-23 1999-09-22 Notetry Ltd Method of operating a floor cleaning device
GB9917348D0 (en) 1999-07-24 1999-09-22 Procter & Gamble Robotic system
US6283034B1 (en) 1999-07-30 2001-09-04 D. Wayne Miles, Jr. Remotely armed ammunition
JP3700487B2 (en) 1999-08-30 2005-09-28 トヨタ自動車株式会社 Vehicle position detecting device
DE69927590D1 (en) 1999-08-31 2006-02-16 Swisscom Ag Bern The mobile robot and control method for a mobile robot
JP2001087182A (en) 1999-09-20 2001-04-03 Mitsubishi Electric Corp Vacuum cleaner
US6480762B1 (en) 1999-09-27 2002-11-12 Olympus Optical Co., Ltd. Medical apparatus supporting system
DE19948974A1 (en) 1999-10-11 2001-04-12 Nokia Mobile Phones Ltd A method for detecting and selecting a sequence of notes, in particular a piece of music
US6530102B1 (en) 1999-10-20 2003-03-11 Tennant Company Scrubber head anti-vibration mounting
JP2001121455A (en) 1999-10-29 2001-05-08 Sony Corp Charge system of and charge control method for mobile robot, charge station, mobile robot and its control method
JP4207336B2 (en) 1999-10-29 2009-01-14 ソニー株式会社 The charging system for a mobile robot, a method of searching the charging station, the mobile robot, connectors, and electrical connection structure
JP2001216482A (en) 1999-11-10 2001-08-10 Matsushita Electric Ind Co Ltd Electric equipment and portable recording medium
US6548982B1 (en) 1999-11-19 2003-04-15 Regents Of The University Of Minnesota Miniature robotic vehicles and methods of controlling same
US6362875B1 (en) 1999-12-10 2002-03-26 Cognax Technology And Investment Corp. Machine vision system and method for inspection, homing, guidance and docking with respect to remote objects
US6513046B1 (en) 1999-12-15 2003-01-28 Tangis Corporation Storing and recalling information to augment human memories
US6263539B1 (en) 1999-12-23 2001-07-24 Taf Baig Carpet/floor cleaning wand and machine
JP4019586B2 (en) 1999-12-27 2007-12-12 富士電機リテイルシステムズ株式会社 Store management systems, information management method and a computer-readable recording medium recording a program for making a computer execute the method
JP2001197008A (en) 2000-01-13 2001-07-19 Tsubakimoto Chain Co Mobile optical communication system, photodetection device, optical communication device, and carrier device
US6467122B2 (en) 2000-01-14 2002-10-22 Bissell Homecare, Inc. Deep cleaner with tool mount
US6146041A (en) 2000-01-19 2000-11-14 Chen; He-Jin Sponge mop with cleaning tank attached thereto
US9128486B2 (en) 2002-01-24 2015-09-08 Irobot Corporation Navigational control system for a robotic device
US8412377B2 (en) 2000-01-24 2013-04-02 Irobot Corporation Obstacle following sensor scheme for a mobile robot
US6332400B1 (en) 2000-01-24 2001-12-25 The United States Of America As Represented By The Secretary Of The Navy Initiating device for use with telemetry systems
GB2358843B (en) * 2000-02-02 2002-01-23 Logical Technologies Ltd An autonomous mobile apparatus for performing work within a pre-defined area
US6418586B2 (en) 2000-02-02 2002-07-16 Alto U.S., Inc. Liquid extraction machine
JP2001289939A (en) 2000-02-02 2001-10-19 Mitsubishi Electric Corp Ultrasonic wave transmitter/receiver and peripheral obstacle detector for vehicle
US6421870B1 (en) 2000-02-04 2002-07-23 Tennant Company Stacked tools for overthrow sweeping
DE10006493C2 (en) 2000-02-14 2002-02-07 Hilti Ag Method and apparatus for optoelectronic distance measurement
US6276478B1 (en) 2000-02-16 2001-08-21 Kathleen Garrubba Hopkins Adherent robot
DE10007864A1 (en) 2000-02-21 2001-08-30 Wittenstein Gmbh & Co Kg Detecting, determining, locating at least one object and/or space involves transmitting spatial coordinates and/or coordinates of any object in space to robot to orient it
US20010025183A1 (en) 2000-02-25 2001-09-27 Ramin Shahidi Methods and apparatuses for maintaining a trajectory in sterotaxi for tracking a target inside a body
US6278918B1 (en) 2000-02-28 2001-08-21 Case Corporation Region of interest selection for a vision guidance system
US6285930B1 (en) 2000-02-28 2001-09-04 Case Corporation Tracking improvement for a vision guidance system
US6490539B1 (en) 2000-02-28 2002-12-03 Case Corporation Region of interest selection for varying distances between crop rows for a vision guidance system
US6373573B1 (en) 2000-03-13 2002-04-16 Lj Laboratories L.L.C. Apparatus for measuring optical characteristics of a substrate and pigments applied thereto
JP2001258807A (en) 2000-03-16 2001-09-25 Sharp Corp Self-traveling vacuum cleaner
US6443509B1 (en) 2000-03-21 2002-09-03 Friendly Robotics Ltd. Tactile sensor
US6540424B1 (en) 2000-03-24 2003-04-01 The Clorox Company Advanced cleaning system
JP2001275908A (en) 2000-03-30 2001-10-09 Matsushita Seiko Co Ltd Cleaning device
JP4032603B2 (en) 2000-03-31 2008-01-16 コニカミノルタセンシング株式会社 3-dimensional measurement device
JP4480843B2 (en) * 2000-04-03 2010-06-16 ソニー株式会社 Legged mobile robot and a control method thereof, as well as relative movement measuring sensor for the legged mobile robot
US20010045883A1 (en) 2000-04-03 2001-11-29 Holdaway Charles R. Wireless digital launch or firing system
JP2001277163A (en) 2000-04-03 2001-10-09 Sony Corp Device and method for controlling robot
WO2001074652A3 (en) 2000-04-04 2002-03-28 Irobot Corp Wheeled platforms
US6870792B2 (en) 2000-04-04 2005-03-22 Irobot Corporation Sonar Scanner
GB2361629B (en) 2000-04-24 2003-01-15 Samsung Kwangju Electronics Co Brush assembly for a vacuum cleaner comprising an edge brush integrated bumper
DE10020503A1 (en) 2000-04-26 2001-10-31 Bsh Bosch Siemens Hausgeraete Machining appliance incorporates vacuum generator between machining appliance and machined surface, with support and working appliance
US6769004B2 (en) 2000-04-27 2004-07-27 Irobot Corporation Method and system for incremental stack scanning
US6845297B2 (en) * 2000-05-01 2005-01-18 Irobot Corporation Method and system for remote control of mobile robot
EP2363775A1 (en) 2000-05-01 2011-09-07 iRobot Corporation Method and system for remote control of mobile robot
WO2001082766B1 (en) * 2000-05-02 2002-07-18 Personal Robotics Inc Autonomous floor mopping apparatus
US6633150B1 (en) 2000-05-02 2003-10-14 Personal Robotics, Inc. Apparatus and method for improving traction for a mobile robot
JP2001320781A (en) 2000-05-10 2001-11-16 Inst Of Physical & Chemical Res Support system using data carrier system
US6454036B1 (en) 2000-05-15 2002-09-24 ′Bots, Inc. Autonomous vehicle navigation system and method
US6854148B1 (en) 2000-05-26 2005-02-15 Poolvernguegen Four-wheel-drive automatic swimming pool cleaner
US6481515B1 (en) 2000-05-30 2002-11-19 The Procter & Gamble Company Autonomous mobile surface treating apparatus
US6385515B1 (en) 2000-06-15 2002-05-07 Case Corporation Trajectory path planner for a vision guidance system
JP2002082720A (en) 2000-06-29 2002-03-22 Inst Of Physical & Chemical Res Method for teaching target position of moving body, movement control method, and method and system for light guidance
US6397429B1 (en) 2000-06-30 2002-06-04 Nilfisk-Advance, Inc. Riding floor scrubber
US6539284B2 (en) 2000-07-25 2003-03-25 Axonn Robotics, Llc Socially interactive autonomous robot
EP1176487A1 (en) * 2000-07-27 2002-01-30 Gmd - Forschungszentrum Informationstechnik Gmbh Autonomously navigating robot system
US6571422B1 (en) 2000-08-01 2003-06-03 The Hoover Company Vacuum cleaner with a microprocessor-based dirt detection circuit
KR100391179B1 (en) 2000-08-02 2003-07-12 한국전력공사 Teleoperated mobile cleanup device for highly radioactive fine waste
US6720879B2 (en) 2000-08-08 2004-04-13 Time-N-Space Technology, Inc. Animal collar including tracking and location device
JP2002073170A (en) 2000-08-25 2002-03-12 Matsushita Electric Ind Co Ltd Movable working robot
US6832407B2 (en) 2000-08-25 2004-12-21 The Hoover Company Moisture indicator for wet pick-up suction cleaner
US7388879B2 (en) 2000-08-28 2008-06-17 Sony Corporation Communication device and communication method network system and robot apparatus
JP3674481B2 (en) 2000-09-08 2005-07-20 松下電器産業株式会社 Self-propelled vacuum cleaner
US7040869B2 (en) 2000-09-14 2006-05-09 Jan W. Beenker Method and device for conveying media
KR20020022444A (en) 2000-09-20 2002-03-27 김대홍 Fuselage and wings and model plane using the same
US20050255425A1 (en) 2000-09-21 2005-11-17 Pierson Paul R Mixing tip for dental materials
US6502657B2 (en) * 2000-09-22 2003-01-07 The Charles Stark Draper Laboratory, Inc. Transformable vehicle
EP1191166A1 (en) 2000-09-26 2002-03-27 THE PROCTER & GAMBLE COMPANY Process of cleaning the inner surface of a water-containing vessel
US6674259B1 (en) 2000-10-06 2004-01-06 Innovation First, Inc. System and method for managing and controlling a robot competition
USD458318S1 (en) 2000-10-10 2002-06-04 Sharper Image Corporation Robot
US6690993B2 (en) 2000-10-12 2004-02-10 R. Foulke Development Company, Llc Reticle storage system
US6658693B1 (en) 2000-10-12 2003-12-09 Bissell Homecare, Inc. Hand-held extraction cleaner with turbine-driven brush
JP3681728B2 (en) 2000-10-30 2005-08-10 トルブヨルン、アーセンTorbjorn Aasen Movable robot
US6615885B1 (en) 2000-10-31 2003-09-09 Irobot Corporation Resilient wheel structure
US20020081937A1 (en) 2000-11-07 2002-06-27 Satoshi Yamada Electronic toy
WO2002039868A1 (en) 2000-11-17 2002-05-23 Duplex Cleaning Machines Pty. Limited Sensors for robotic devices
US6571415B2 (en) 2000-12-01 2003-06-03 The Hoover Company Random motion cleaner
US6572711B2 (en) 2000-12-01 2003-06-03 The Hoover Company Multi-purpose position sensitive floor cleaning device
WO2002045915A1 (en) 2000-12-04 2002-06-13 Abb Ab Robot system
US6684511B2 (en) 2000-12-14 2004-02-03 Wahl Clipper Corporation Hair clipping device with rotating bladeset having multiple cutting edges
JP3946499B2 (en) 2000-12-27 2007-07-18 フジノン株式会社 Posture detecting method and device using the same object to be observed
US6661239B1 (en) 2001-01-02 2003-12-09 Irobot Corporation Capacitive sensor systems and methods with increased resolution and automatic calibration
US6388013B1 (en) 2001-01-04 2002-05-14 Equistar Chemicals, Lp Polyolefin fiber compositions
US6444003B1 (en) 2001-01-08 2002-09-03 Terry Lee Sutcliffe Filter apparatus for sweeper truck hopper
JP2002204768A (en) 2001-01-12 2002-07-23 Matsushita Electric Ind Co Ltd Self-propelled cleaner
JP4479101B2 (en) 2001-01-12 2010-06-09 パナソニック株式会社 Self-propelled vacuum cleaner
FR2820216B1 (en) 2001-01-26 2003-04-25 Wany Sa Method and obstacle detection device and distance measuring infrared radiation
EP1228734A3 (en) 2001-02-01 2003-06-11 Pierangelo Bertola Crumb collecting brush
WO2002062194A1 (en) 2001-02-07 2002-08-15 Zucchetti Centro Sistemi S.P.A. Automatic floor cleaning device
USD471243S1 (en) 2001-02-09 2003-03-04 Irobot Corporation Robot
US6530117B2 (en) 2001-02-12 2003-03-11 Robert A. Peterson Wet vacuum
US6810305B2 (en) 2001-02-16 2004-10-26 The Procter & Gamble Company Obstruction management system for robots
JP4438237B2 (en) 2001-02-22 2010-03-24 ソニー株式会社 Receiving apparatus and method, recording medium, and program
GB0104456D0 (en) 2001-02-23 2001-04-11 Ind Controls Systems Ltd Apparatus and method for obtaining three-dimensional positional data from a two-dimensional captured image
ES2225775T5 (en) 2001-02-24 2008-04-01 Dyson Technology Limited Vacuum collection chamber.
US7647144B2 (en) * 2001-02-28 2010-01-12 Aktiebolaget Electrolux Obstacle sensing system for an autonomous cleaning apparatus
WO2002067744A1 (en) 2001-02-28 2002-09-06 Aktiebolaget Electrolux Wheel support arrangement for an autonomous cleaning apparatus
DE10110907A1 (en) 2001-03-07 2002-09-19 Kaercher Gmbh & Co Alfred Floor cleaning device
DE10110905A1 (en) 2001-03-07 2002-10-02 Kaercher Gmbh & Co Alfred Harrow, especially floor cleaning device
DE10110906A1 (en) 2001-03-07 2002-09-19 Kaercher Gmbh & Co Alfred sweeper
US20040190376A1 (en) 2001-03-15 2004-09-30 Jarl Hulden Sonar transducer
CA2441223A1 (en) 2001-03-15 2002-09-26 Aktiebolaget Electrolux Efficient navigation of autonomous carriers
CA2441076A1 (en) 2001-03-15 2002-09-26 Aktiebolaget Electrolux System for determining the state of a carrier on a field of operation
EP1379155B1 (en) 2001-03-16 2013-09-25 Vision Robotics Corporation Autonomous mobile canister vacuum cleaner
WO2002075350A1 (en) 2001-03-20 2002-09-26 Danaher Motion Särö AB Method and device for determining an angular position of a reflector
JP3849442B2 (en) 2001-03-27 2006-11-22 株式会社日立製作所 Self-propelled vacuum cleaner
DE10116892A1 (en) 2001-04-04 2002-10-17 Outokumpu Oy Method of conveying granular solids
JP2002369778A (en) 2001-04-13 2002-12-24 Yashima Denki Co Ltd Dust detecting device and vacuum cleaner
KR100437372B1 (en) 2001-04-18 2004-06-25 삼성광주전자 주식회사 Robot cleaning System using by mobile communication network
RU2218859C2 (en) 2001-04-18 2003-12-20 Самсунг Гванджу Электроникс Ко., Лтд. Cleaning robot, system with cleaning robot, outer charging device and method for returning cleaning robot to outer charging device
US6687571B1 (en) 2001-04-24 2004-02-03 Sandia Corporation Cooperating mobile robots
US6438456B1 (en) 2001-04-24 2002-08-20 Sandia Corporation Portable control device for networked mobile robots
US6408226B1 (en) 2001-04-24 2002-06-18 Sandia Corporation Cooperative system and method using mobile robots for testing a cooperative search controller
FR2823842B1 (en) 2001-04-24 2003-09-05 Romain Granger Measuring method for determining the position and orientation of a movable assembly, and apparatus for carrying out said method
JP2002323925A (en) 2001-04-26 2002-11-08 Matsushita Electric Ind Co Ltd Moving working robot
US6540607B2 (en) 2001-04-26 2003-04-01 Midway Games West Video game position and orientation detection system
US20020159051A1 (en) 2001-04-30 2002-10-31 Mingxian Guo Method for optical wavelength position searching and tracking
US7809944B2 (en) 2001-05-02 2010-10-05 Sony Corporation Method and apparatus for providing information for decrypting content, and program executed on information processor
US6487474B1 (en) 2001-05-10 2002-11-26 International Business Machines Corporation Automated data storage library with multipurpose slots providing user-selected control path to shared robotic device
US6711280B2 (en) 2001-05-25 2004-03-23 Oscar M. Stafsudd Method and apparatus for intelligent ranging via image subtraction
EP1408729B1 (en) 2001-05-28 2016-10-26 Husqvarna AB Improvement to a robotic lawnmower
JP4802397B2 (en) 2001-05-30 2011-10-26 コニカミノルタホールディングス株式会社 Imaging system, and the operating device
US6763282B2 (en) 2001-06-04 2004-07-13 Time Domain Corp. Method and system for controlling a robot
JP2002355206A (en) 2001-06-04 2002-12-10 Matsushita Electric Ind Co Ltd Traveling vacuum cleaner
JP4017840B2 (en) 2001-06-05 2007-12-05 松下電器産業株式会社 Self-propelled vacuum cleaner
US6901624B2 (en) 2001-06-05 2005-06-07 Matsushita Electric Industrial Co., Ltd. Self-moving cleaner
JP2002366227A (en) 2001-06-05 2002-12-20 Matsushita Electric Ind Co Ltd Movable working robot
US6670817B2 (en) 2001-06-07 2003-12-30 Heidelberger Druckmaschinen Ag Capacitive toner level detection
WO2002101018A3 (en) 2001-06-11 2003-10-16 Hutchinson Fred Cancer Res Methods for inducing reversible stasis
US8396592B2 (en) 2001-06-12 2013-03-12 Irobot Corporation Method and system for multi-mode coverage for an autonomous robot
US7663333B2 (en) 2001-06-12 2010-02-16 Irobot Corporation Method and system for multi-mode coverage for an autonomous robot
US6473167B1 (en) 2001-06-14 2002-10-29 Ascension Technology Corporation Position and orientation determination using stationary fan beam sources and rotating mirrors to sweep fan beams
US6507773B2 (en) * 2001-06-14 2003-01-14 Sharper Image Corporation Multi-functional robot with remote and video system
US6685092B2 (en) 2001-06-15 2004-02-03 Symbol Technologies, Inc. Molded imager optical package and miniaturized linear sensor-based code reading engines
JP2003005296A (en) 2001-06-18 2003-01-08 Noritsu Koki Co Ltd Photographic processing device
US6735812B2 (en) 2002-02-22 2004-05-18 Tennant Company Dual mode carpet cleaning apparatus utilizing an extraction device and a soil transfer cleaning medium
US6604021B2 (en) 2001-06-21 2003-08-05 Advanced Telecommunications Research Institute International Communication robot
JP4553524B2 (en) 2001-06-27 2010-09-29 フィグラ株式会社 Liquid coating method
JP2003010076A (en) 2001-06-27 2003-01-14 Figla Co Ltd Vacuum cleaner
US6622465B2 (en) 2001-07-10 2003-09-23 Deere & Company Apparatus and method for a material collection fill indicator
JP4601215B2 (en) 2001-07-16 2010-12-22 三洋電機サービス株式会社 Cryogenic refrigeration system
US20030015232A1 (en) * 2001-07-23 2003-01-23 Thomas Nguyen Portable car port
JP2003036116A (en) 2001-07-25 2003-02-07 Toshiba Tec Corp Autonomous travel robot
US6735811B2 (en) 2001-07-30 2004-05-18 Tennant Company Cleaning liquid dispensing system for a hard floor surface cleaner
US6671925B2 (en) * 2001-07-30 2004-01-06 Tennant Company Chemical dispenser for a hard floor surface cleaner
US6585827B2 (en) 2001-07-30 2003-07-01 Tennant Company Apparatus and method of use for cleaning a hard floor surface utilizing an aerated cleaning liquid
US7051399B2 (en) * 2001-07-30 2006-05-30 Tennant Company Cleaner cartridge
JP2003038401A (en) 2001-08-01 2003-02-12 Toshiba Tec Corp Cleaner
JP2003038402A (en) 2001-08-02 2003-02-12 Toshiba Tec Corp Cleaner
JP2003047579A (en) 2001-08-06 2003-02-18 Toshiba Tec Corp Vacuum cleaner
KR100420171B1 (en) * 2001-08-07 2004-03-02 삼성광주전자 주식회사 Robot cleaner and system therewith and method of driving thereof
FR2828589B1 (en) 2001-08-07 2003-12-05 France Telecom An electrical connection system between a vehicle and a charging station or the like
US6580246B2 (en) 2001-08-13 2003-06-17 Steven Jacobs Robot touch shield
JP2003061882A (en) 2001-08-28 2003-03-04 Matsushita Electric Ind Co Ltd Self-propelled vacuum cleaner
US20030168081A1 (en) 2001-09-06 2003-09-11 Timbucktoo Mfg., Inc. Motor-driven, portable, adjustable spray system for cleaning hard surfaces
JP2003084994A (en) 2001-09-12 2003-03-20 Olympus Optical Co Ltd Medical system
EP1437958B1 (en) 2001-09-14 2005-11-16 Vorwerk & Co. Interholding GmbH Automatically displaceable floor-type dust collector and combination of said collector and a base station
JP2003179556A (en) 2001-09-21 2003-06-27 Casio Comput Co Ltd Information transmission method, information transmission system, imaging apparatus and information transmission method
US7167775B2 (en) * 2001-09-26 2007-01-23 F Robotics Acquisitions, Ltd. Robotic vacuum cleaner
US6624744B1 (en) 2001-10-05 2003-09-23 William Neil Wilson Golf cart keyless control system
US6980229B1 (en) 2001-10-16 2005-12-27 Ebersole Jr John F System for precise rotational and positional tracking
GB0126492D0 (en) 2001-11-03 2002-01-02 Dyson Ltd An autonomous machine
GB0126497D0 (en) 2001-11-03 2002-01-02 Dyson Ltd An autonomous machine
DE10155271A1 (en) 2001-11-09 2003-05-28 Bosch Gmbh Robert Common rail injector
US6776817B2 (en) 2001-11-26 2004-08-17 Honeywell International Inc. Airflow sensor, system and method for detecting airflow within an air handling system
KR100449710B1 (en) 2001-12-10 2004-09-22 삼성전자주식회사 Remote pointing method and apparatus therefor
JP3626724B2 (en) 2001-12-14 2005-03-09 株式会社日立製作所 Self-propelled vacuum cleaner
US6860206B1 (en) 2001-12-14 2005-03-01 Irobot Corporation Remote digital firing system
US8375838B2 (en) 2001-12-14 2013-02-19 Irobot Corporation Remote digital firing system
JP3986310B2 (en) 2001-12-19 2007-10-03 シャープ株式会社 Parent-child type vacuum cleaner
JP3907169B2 (en) 2001-12-21 2007-04-18 富士フイルム株式会社 Mobile robot
JP2003190064A (en) 2001-12-25 2003-07-08 Duskin Co Ltd Self-traveling vacuum cleaner
US7335271B2 (en) 2002-01-02 2008-02-26 Lewis & Clark College Adhesive microstructure and method of forming same
US7571511B2 (en) 2002-01-03 2009-08-11 Irobot Corporation Autonomous floor-cleaning robot
US6886651B1 (en) 2002-01-07 2005-05-03 Massachusetts Institute Of Technology Material transportation system
USD474312S1 (en) 2002-01-11 2003-05-06 The Hoover Company Robotic vacuum cleaner
DE60222471D1 (en) 2002-01-18 2007-10-25 Hitachi Ltd radar device
US8428778B2 (en) 2002-09-13 2013-04-23 Irobot Corporation Navigational control system for a robotic device
US6674687B2 (en) * 2002-01-25 2004-01-06 Navcom Technology, Inc. System and method for navigation using two-way ultrasonic positioning
US6856811B2 (en) 2002-02-01 2005-02-15 Warren L. Burdue Autonomous portable communication network
US6844606B2 (en) 2002-02-04 2005-01-18 Delphi Technologies, Inc. Surface-mount package for an optical sensing device and method of manufacture
JP2003241836A (en) 2002-02-19 2003-08-29 Keio Gijuku Control method and apparatus for free-running mobile unit
US6756703B2 (en) 2002-02-27 2004-06-29 Chi Che Chang Trigger switch module
JP3863447B2 (en) 2002-03-08 2006-12-27 インターナショナル・ビジネス・マシーンズ・コーポレーションInternational Business Maschines Corporation Authentication system, firmware device, electrical appliances, and authentication method
JP3812463B2 (en) 2002-03-08 2006-08-23 株式会社日立製作所 Direction detecting device and it equipped with self-propelled cleaner
US6658354B2 (en) 2002-03-15 2003-12-02 American Gnc Corporation Interruption free navigator
JP2002360482A (en) 2002-03-15 2002-12-17 Matsushita Electric Ind Co Ltd Self-propelled cleaner
WO2003081392A3 (en) 2002-03-21 2004-07-01 Rapistan System Advertising Co Graphical system configuration program for material handling
JP4032793B2 (en) 2002-03-27 2008-01-16 ソニー株式会社 Charging system and charging control method, the robot apparatus, and a charging control program, and a recording medium
US7103457B2 (en) 2002-03-28 2006-09-05 Dean Technologies, Inc. Programmable lawn mower
JP2004001162A (en) 2002-03-28 2004-01-08 Fuji Photo Film Co Ltd Pet robot charging system, receiving arrangement, robot, and robot system
JP2003296855A (en) 2002-03-29 2003-10-17 Toshiba Corp Monitoring device
KR20030082040A (en) 2002-04-16 2003-10-22 삼성광주전자 주식회사 Robot cleaner
US7117067B2 (en) 2002-04-16 2006-10-03 Irobot Corporation System and methods for adaptive control of robotic devices
JP2003304992A (en) 2002-04-17 2003-10-28 Hitachi Ltd Self-running type vacuum cleaner
US20040030570A1 (en) 2002-04-22 2004-02-12 Neal Solomon System, methods and apparatus for leader-follower model of mobile robotic system aggregation
US20040068416A1 (en) 2002-04-22 2004-04-08 Neal Solomon System, method and apparatus for implementing a mobile sensor network
US20040030448A1 (en) 2002-04-22 2004-02-12 Neal Solomon System, methods and apparatus for managing external computation and sensor resources applied to mobile robotic network
US20040068351A1 (en) 2002-04-22 2004-04-08 Neal Solomon System, methods and apparatus for integrating behavior-based approach into hybrid control model for use with mobile robotic vehicles
US20040030571A1 (en) 2002-04-22 2004-02-12 Neal Solomon System, method and apparatus for automated collective mobile robotic vehicles used in remote sensing surveillance
US7047861B2 (en) 2002-04-22 2006-05-23 Neal Solomon System, methods and apparatus for managing a weapon system
US20040068415A1 (en) 2002-04-22 2004-04-08 Neal Solomon System, methods and apparatus for coordination of and targeting for mobile robotic vehicles
US6929548B2 (en) 2002-04-23 2005-08-16 Xiaoling Wang Apparatus and a method for more realistic shooting video games on computers or similar devices
JP2003310509A (en) 2002-04-23 2003-11-05 Hitachi Ltd Mobile cleaner
US6691058B2 (en) 2002-04-29 2004-02-10 Hewlett-Packard Development Company, L.P. Determination of pharmaceutical expiration date
US7113847B2 (en) 2002-05-07 2006-09-26 Royal Appliance Mfg. Co. Robotic vacuum with removable portable vacuum and semi-automated environment mapping
US6836701B2 (en) 2002-05-10 2004-12-28 Royal Appliance Mfg. Co. Autonomous multi-platform robotic system
JP2003330543A (en) 2002-05-17 2003-11-21 Toshiba Tec Corp Charging type autonomous moving system
EP1508409A1 (en) 2002-05-20 2005-02-23 Sony Corporation Robot device and robot control method
GB0211644D0 (en) 2002-05-21 2002-07-03 Wesby Philip B System and method for remote asset management
DE10226853B3 (en) 2002-06-15 2004-02-19 Kuka Roboter Gmbh A method for limiting the force action of a robot part
US6967275B2 (en) 2002-06-25 2005-11-22 Irobot Corporation Song-matching system and method
KR100556612B1 (en) 2002-06-29 2006-03-06 삼성전자주식회사 Apparatus and method of localization using laser
DE10231387A1 (en) 2002-07-08 2004-02-12 Alfred Kärcher Gmbh & Co. Kg Floor cleaning device
DE10231384A1 (en) 2002-07-08 2004-02-05 Alfred Kärcher Gmbh & Co. Kg Method of operating a floor cleaning system and the floor-cleaning system for applying the method
DE10231390A1 (en) 2002-07-08 2004-02-05 Alfred Kärcher Gmbh & Co. Kg Suction device for cleaning purposes
DE10231386B4 (en) 2002-07-08 2004-05-06 Alfred Kärcher Gmbh & Co. Kg Sensor device and self-propelled floor cleaning device with a sensor device
US20050150519A1 (en) 2002-07-08 2005-07-14 Alfred Kaercher Gmbh & Co. Kg Method for operating a floor cleaning system, and floor cleaning system for use of the method
DE10231388A1 (en) 2002-07-08 2004-02-05 Alfred Kärcher Gmbh & Co. Kg Tillage system
US6925357B2 (en) 2002-07-25 2005-08-02 Intouch Health, Inc. Medical tele-robotic system
KR100483548B1 (en) * 2002-07-26 2005-04-15 삼성광주전자 주식회사 Robot cleaner and system and method of controlling thereof
US6741364B2 (en) 2002-08-13 2004-05-25 Harris Corporation Apparatus for determining relative positioning of objects and related methods
US20040031113A1 (en) 2002-08-14 2004-02-19 Wosewick Robert T. Robotic surface treating device with non-circular housing
US7085623B2 (en) 2002-08-15 2006-08-01 Asm International Nv Method and system for using short ranged wireless enabled computers as a service tool
US7162056B2 (en) 2002-08-16 2007-01-09 Evolution Robotics, Inc. Systems and methods for the automated sensing of motion in a mobile robot using visual data
USD478884S1 (en) 2002-08-23 2003-08-26 Motorola, Inc. Base for a cordless telephone
US7103447B2 (en) 2002-09-02 2006-09-05 Sony Corporation Robot apparatus, and behavior controlling method for robot apparatus
US7054716B2 (en) 2002-09-06 2006-05-30 Royal Appliance Mfg. Co. Sentry robot system
US20040143919A1 (en) 2002-09-13 2004-07-29 Wildwood Industries, Inc. Floor sweeper having a viewable receptacle
JP2004109767A (en) 2002-09-20 2004-04-08 Ricoh Co Ltd Image display device, image forming optical device, and image forming optical system for image display device
EP1548532A4 (en) 2002-10-01 2007-08-08 Fujitsu Ltd Robot
JP2004123040A (en) 2002-10-07 2004-04-22 Figla Co Ltd Omnidirectional moving vehicle
US7054718B2 (en) 2002-10-11 2006-05-30 Sony Corporation Motion editing apparatus and method for legged mobile robot and computer program
US6871115B2 (en) 2002-10-11 2005-03-22 Taiwan Semiconductor Manufacturing Co., Ltd Method and apparatus for monitoring the operation of a wafer handling robot
US7303010B2 (en) 2002-10-11 2007-12-04 Intelligent Robotic Corporation Apparatus and method for an autonomous robotic system for performing activities in a well
US6804579B1 (en) * 2002-10-16 2004-10-12 Abb, Inc. Robotic wash cell using recycled pure water
KR100492577B1 (en) 2002-10-22 2005-06-03 엘지전자 주식회사 Suction head of robot cleaner
KR100459465B1 (en) 2002-10-22 2004-12-03 엘지전자 주식회사 Dust suction structure of robot cleaner
US7069124B1 (en) 2002-10-28 2006-06-27 Workhorse Technologies, Llc Robotic modeling of voids
KR100466321B1 (en) 2002-10-31 2005-01-14 삼성광주전자 주식회사 Robot cleaner, system thereof and method for controlling the same
KR100468107B1 (en) 2002-10-31 2005-01-26 삼성광주전자 주식회사 Robot cleaner system having external charging apparatus and method for docking with the same apparatus
JP2004148021A (en) 2002-11-01 2004-05-27 Hitachi Home & Life Solutions Inc Self-traveling cleaner
US7079924B2 (en) 2002-11-07 2006-07-18 The Regents Of The University Of California Vision-based obstacle avoidance
GB0226242D0 (en) 2002-11-11 2002-12-18 Qinetiq Ltd Ranging apparatus
US7032469B2 (en) 2002-11-12 2006-04-25 Raytheon Company Three axes line-of-sight transducer
US20050209736A1 (en) 2002-11-13 2005-09-22 Figla Co., Ltd. Self-propelled working robot
KR100542340B1 (en) 2002-11-18 2006-01-11 삼성전자주식회사 home network system and method for controlling home network system
JP2004166968A (en) 2002-11-20 2004-06-17 Zojirushi Corp Self-propelled cleaning robot
US7320149B1 (en) * 2002-11-22 2008-01-22 Bissell Homecare, Inc. Robotic extraction cleaner with dusting pad
US7346428B1 (en) 2002-11-22 2008-03-18 Bissell Homecare, Inc. Robotic sweeper cleaner with dusting pad
JP3885019B2 (en) 2002-11-29 2007-02-21 株式会社東芝 Security system and a mobile robot
US7496665B2 (en) 2002-12-11 2009-02-24 Broadcom Corporation Personal access and control of media peripherals on a media exchange network
GB0229620D0 (en) 2002-12-19 2003-01-22 Nokia Corp Encoder
JP3731123B2 (en) 2002-12-20 2006-01-05 新菱冷熱工業株式会社 Position detecting method and apparatus object
DE10261788B3 (en) 2002-12-23 2004-01-22 Alfred Kärcher Gmbh & Co. Kg Mobile harrow
DE10262191A1 (en) 2002-12-23 2006-12-14 Alfred Kärcher Gmbh & Co. Kg Mobile harrow
JP3884377B2 (en) * 2002-12-27 2007-02-21 ジーイー・メディカル・システムズ・グローバル・テクノロジー・カンパニー・エルエルシー X-ray imaging apparatus
JP2004219185A (en) 2003-01-14 2004-08-05 Meidensha Corp Electrical inertia evaluation device for dynamometer and its method
US20040148419A1 (en) 2003-01-23 2004-07-29 Chen Yancy T. Apparatus and method for multi-user entertainment
US7146682B2 (en) 2003-01-31 2006-12-12 The Hoover Company Powered edge cleaner
JP2004237392A (en) 2003-02-05 2004-08-26 Sony Corp Robotic device and expression method of robotic device
JP2004237075A (en) 2003-02-06 2004-08-26 Samsung Kwangju Electronics Co Ltd Robot cleaner system provided with external charger and connection method for robot cleaner to external charger
KR100485696B1 (en) 2003-02-07 2005-04-28 삼성광주전자 주식회사 Location mark detecting method for a robot cleaner and a robot cleaner using the same method
GB2398394B (en) 2003-02-14 2006-05-17 Dyson Ltd An autonomous machine
JP2004267236A (en) 2003-03-05 2004-09-30 Hitachi Home & Life Solutions Inc Self-traveling type vacuum cleaner and charging device used for the same
US20040181706A1 (en) 2003-03-13 2004-09-16 Chen Yancy T. Time-controlled variable-function or multi-function apparatus and methods
US20060020369A1 (en) * 2004-03-11 2006-01-26 Taylor Charles E Robot vacuum cleaner
US20050010331A1 (en) * 2003-03-14 2005-01-13 Taylor Charles E. Robot vacuum with floor type modes
US7801645B2 (en) 2003-03-14 2010-09-21 Sharper Image Acquisition Llc Robotic vacuum cleaner with edge and object detection system
US20050273967A1 (en) 2004-03-11 2005-12-15 Taylor Charles E Robot vacuum with boundary cones
KR100492590B1 (en) 2003-03-14 2005-06-03 엘지전자 주식회사 Auto charge system and return method for robot
US20040200505A1 (en) 2003-03-14 2004-10-14 Taylor Charles E. Robot vac with retractable power cord
JP2004275468A (en) 2003-03-17 2004-10-07 Hitachi Home & Life Solutions Inc Self-traveling vacuum cleaner and method of operating the same
JP3484188B1 (en) 2003-03-31 2004-01-06 貴幸 関島 Steam jet cleaning device
KR20040086940A (en) * 2003-04-03 2004-10-13 엘지전자 주식회사 Mobile robot in using image sensor and his mobile distance mesurement method
US7627197B2 (en) 2003-04-07 2009-12-01 Honda Motor Co., Ltd. Position measurement method, an apparatus, a computer program and a method for generating calibration information
KR100486737B1 (en) 2003-04-08 2005-05-03 삼성전자주식회사 Method and apparatus for generating and tracing cleaning trajectory for home cleaning robot
US7057120B2 (en) 2003-04-09 2006-06-06 Research In Motion Limited Shock absorbent roller thumb wheel
US20040221790A1 (en) 2003-05-02 2004-11-11 Sinclair Kenneth H. Method and apparatus for optical odometry
US6975246B1 (en) 2003-05-13 2005-12-13 Itt Manufacturing Enterprises, Inc. Collision avoidance using limited range gated video
JP4358861B2 (en) 2003-06-11 2009-11-04 ドレーガー メディカル システムズ インコーポレイテッドDraeger Medical Systems Inc. Mobile patient monitoring systems with location identification capabilities
US6888333B2 (en) 2003-07-02 2005-05-03 Intouch Health, Inc. Holonomic platform for a robot
US7133746B2 (en) * 2003-07-11 2006-11-07 F Robotics Acquistions, Ltd. Autonomous machine for docking with a docking station and method for docking
DE10331874A1 (en) 2003-07-14 2005-03-03 Robert Bosch Gmbh Remote programming of a program-controlled device
DE10333395A1 (en) 2003-07-16 2005-02-17 Alfred Kärcher Gmbh & Co. Kg Floor Cleaning System
GB2404139B (en) * 2003-07-24 2005-08-31 Samsung Kwangju Electronics Co Dust receptacle for a robotic vacuum cleaner
JP2005040596A (en) * 2003-07-24 2005-02-17 Samsung Kwangju Electronics Co Ltd Robot cleaner
KR100478681B1 (en) 2003-07-29 2005-03-25 삼성광주전자 주식회사 an robot-cleaner equipped with floor-disinfecting function
CN2637136Y (en) 2003-08-11 2004-09-01 泰怡凯电器(苏州)有限公司 Self-positioning mechanism for robot
JP4271193B2 (en) 2003-08-12 2009-06-03 株式会社国際電気通信基礎技術研究所 Control system for communication robot
US7027893B2 (en) 2003-08-25 2006-04-11 Ati Industrial Automation, Inc. Robotic tool coupler rapid-connect bus
US20070061041A1 (en) 2003-09-02 2007-03-15 Zweig Stephen E Mobile robot with wireless location sensing apparatus
US7174238B1 (en) 2003-09-02 2007-02-06 Stephen Eliot Zweig Mobile robotic system with web server and digital radio links
US7784147B2 (en) 2003-09-05 2010-08-31 Brunswick Bowling & Billiards Corporation Bowling lane conditioning machine
CA2537850C (en) * 2003-09-05 2008-02-26 Brunswick Bowling & Billiards Corporation Apparatus and method for conditioning a bowling lane using precision delivery injectors
US7225501B2 (en) 2003-09-17 2007-06-05 The Hoover Company Brush assembly for a cleaning device
JP2005088179A (en) 2003-09-22 2005-04-07 Honda Motor Co Ltd Autonomous mobile robot system
US7030768B2 (en) 2003-09-30 2006-04-18 Wanie Andrew J Water softener monitoring device
US7660650B2 (en) 2003-10-08 2010-02-09 Figla Co., Ltd. Self-propelled working robot having horizontally movable work assembly retracting in different speed based on contact sensor input on the assembly
US7246405B2 (en) 2003-10-09 2007-07-24 Jason Yan Self-moving vacuum cleaner with moveable intake nozzle
JP2005118354A (en) 2003-10-17 2005-05-12 Matsushita Electric Ind Co Ltd House interior cleaning system and operation method
US7392566B2 (en) 2003-10-30 2008-07-01 Gordon Evan A Cleaning machine for cleaning a surface
DE60319542D1 (en) 2003-11-07 2008-04-17 Harman Becker Automotive Sys Methods and apparatus for controlling access to encrypted data services for an entertainment and information processing device in a vehicle
DE10357637A1 (en) 2003-12-10 2005-07-07 Vorwerk & Co. Interholding Gmbh A self-propelled or verfahrendes sweeping device as well as combination of a sweeper with a base station
DE10357636B4 (en) 2003-12-10 2013-05-08 Vorwerk & Co. Interholding Gmbh Automatically moveable floor dust collecting device
DE10357635B4 (en) 2003-12-10 2013-10-31 Vorwerk & Co. Interholding Gmbh Floor cleaning device
US7201786B2 (en) 2003-12-19 2007-04-10 The Hoover Company Dust bin and filter for robotic vacuum cleaner
KR20050063546A (en) 2003-12-22 2005-06-28 엘지전자 주식회사 Robot cleaner and operating method thereof
DE602004019625D1 (en) 2003-12-22 2009-04-09 Calzoni Srl An optical device for aircraft Gleitwinkelanzeige
EP1553472A1 (en) 2003-12-31 2005-07-13 Alcatel Remotely controlled vehicle using wireless LAN
US7328196B2 (en) 2003-12-31 2008-02-05 Vanderbilt University Architecture for multiple interacting robot intelligences
KR20050072300A (en) * 2004-01-06 2005-07-11 삼성전자주식회사 Cleaning robot and control method thereof
US7624473B2 (en) 2004-01-07 2009-12-01 The Hoover Company Adjustable flow rate valve for a cleaning apparatus
JP2005210199A (en) 2004-01-20 2005-08-04 Alps Electric Co Ltd Inter-terminal connection method in radio network
DE102004004505B9 (en) 2004-01-22 2010-08-05 Alfred Kärcher Gmbh & Co. Kg Soil cultivation device and method for controlling
EP1711873B1 (en) 2004-01-28 2012-12-19 iRobot Corporation Debris sensor for cleaning apparatus
US20050183230A1 (en) 2004-01-30 2005-08-25 Funai Electric Co., Ltd. Self-propelling cleaner
JP2005211360A (en) 2004-01-30 2005-08-11 Funai Electric Co Ltd Self-propelled cleaner
JP2005211493A (en) 2004-01-30 2005-08-11 Funai Electric Co Ltd Self-propelled cleaner
JP2005211365A (en) 2004-01-30 2005-08-11 Funai Electric Co Ltd Autonomous traveling robot cleaner
JP2005211364A (en) 2004-01-30 2005-08-11 Funai Electric Co Ltd Self-propelled cleaner
DE602005017749D1 (en) 2004-02-03 2009-12-31 F Robotics Acquisitions Ltd Robot dock station and robots for use with it
WO2005077244A1 (en) 2004-02-04 2005-08-25 S. C. Johnson & Son, Inc. Surface treating device with cartridge-based cleaning system
US8045494B2 (en) 2004-02-06 2011-10-25 Koninklijke Philips Electronics N.V. System and method for hibernation mode for beaconing devices
JP2005224263A (en) 2004-02-10 2005-08-25 Funai Electric Co Ltd Self-traveling cleaner
JP2005224265A (en) 2004-02-10 2005-08-25 Funai Electric Co Ltd Self-traveling vacuum cleaner
DE102004007677B4 (en) 2004-02-16 2011-11-17 Miele & Cie. Kg A vacuum cleaner nozzle with a dust flow display device
JP2005230032A (en) 2004-02-17 2005-09-02 Funai Electric Co Ltd Autonomous running robot cleaner
KR100561863B1 (en) 2004-02-19 2006-03-16 삼성전자주식회사 Navigation method and navigation apparatus using virtual sensor for mobile robot
KR100571834B1 (en) 2004-02-27 2006-04-17 삼성전자주식회사 Method and apparatus of detecting dust on the floor in a robot for cleaning
DE102004010827B4 (en) 2004-02-27 2006-01-05 Alfred Kärcher Gmbh & Co. Kg Soil cultivation device and method for controlling
JP4309785B2 (en) 2004-03-08 2009-08-05 フィグラ株式会社 Vacuum cleaner
US7360277B2 (en) 2004-03-24 2008-04-22 Oreck Holdings, Llc Vacuum cleaner fan unit and access aperture
US7148458B2 (en) 2004-03-29 2006-12-12 Evolution Robotics, Inc. Circuit for estimating position and orientation of a mobile object
US7535071B2 (en) 2004-03-29 2009-05-19 Evolution Robotics, Inc. System and method of integrating optics into an IC package
US7603744B2 (en) 2004-04-02 2009-10-20 Royal Appliance Mfg. Co. Robotic appliance with on-board joystick sensor and associated methods of operation
US7617557B2 (en) 2004-04-02 2009-11-17 Royal Appliance Mfg. Co. Powered cleaning appliance
US7400108B2 (en) 2004-04-15 2008-07-15 University Of Utah Research Foundation System and method for controlling modular robots
US7640624B2 (en) 2004-04-16 2010-01-05 Panasonic Corporation Of North America Dirt cup with dump door in bottom wall and dump door actuator on top wall
US7352153B2 (en) 2004-04-20 2008-04-01 Jason Yan Mobile robotic system and battery charging method therefor
US7937800B2 (en) 2004-04-21 2011-05-10 Jason Yan Robotic vacuum cleaner
US7041029B2 (en) 2004-04-23 2006-05-09 Alto U.S. Inc. Joystick controlled scrubber
JP2005346700A (en) 2004-05-07 2005-12-15 Figla Co Ltd Self-propelled working robot
US7208697B2 (en) 2004-05-20 2007-04-24 Lincoln Global, Inc. System and method for monitoring and controlling energy usage
CA2569714A1 (en) 2004-06-08 2005-12-22 Dartdevices Corporation Architecture, apparatus and method for device team recruitment and content renditioning for universal device interoperability platform
JP4163150B2 (en) 2004-06-10 2008-10-08 日立アプライアンス株式会社 Self-propelled vacuum cleaner
US7778640B2 (en) 2004-06-25 2010-08-17 Lg Electronics Inc. Method of communicating data in a wireless mobile communication system
US7254864B2 (en) * 2004-07-01 2007-08-14 Royal Appliance Mfg. Co. Hard floor cleaner
JP2006026028A (en) * 2004-07-14 2006-02-02 Sanyo Electric Co Ltd Cleaner
US20060020370A1 (en) * 2004-07-22 2006-01-26 Shai Abramson System and method for confining a robot
US6993954B1 (en) 2004-07-27 2006-02-07 Tekscan, Incorporated Sensor equilibration and calibration system and method
JP4201747B2 (en) 2004-07-29 2008-12-24 三洋電機株式会社 Self-propelled vacuum cleaner
DE102004038074B3 (en) 2004-07-29 2005-06-30 Alfred Kärcher Gmbh & Co. Kg Self-propelled cleaning robot for floor surfaces has driven wheel rotated in arc about eccentric steering axis upon abutting obstacle in movement path of robot
KR100641113B1 (en) 2004-07-30 2006-11-02 엘지전자 주식회사 Mobile robot and his moving control method
JP4268911B2 (en) 2004-08-04 2009-05-27 日立アプライアンス株式会社 Self-propelled vacuum cleaner
KR100601960B1 (en) 2004-08-05 2006-07-14 삼성전자주식회사 Simultaneous localization and map building method for robot
DE102004041021B3 (en) 2004-08-17 2005-08-25 Alfred Kärcher Gmbh & Co. Kg Floor cleaning system with self-propelled, automatically-controlled roller brush sweeper and central dirt collection station, reverses roller brush rotation during dirt transfer and battery charging
GB0418376D0 (en) 2004-08-18 2004-09-22 Loc8Tor Ltd Locating system
US20060042042A1 (en) 2004-08-26 2006-03-02 Mertes Richard H Hair ingestion device and dust protector for vacuum cleaner
EP1796879A2 (en) 2004-08-27 2007-06-20 Sharper Image Corporation Robot cleaner with improved vacuum unit
KR100677252B1 (en) 2004-09-23 2007-02-02 엘지전자 주식회사 Remote observation system and method in using robot cleaner
KR100664053B1 (en) 2004-09-23 2007-01-03 엘지전자 주식회사 Cleaning tool auto change system and method for robot cleaner
DE102004046383B4 (en) 2004-09-24 2009-06-18 Stein & Co Gmbh Apparatus for floor care appliances brush roller of
KR100754385B1 (en) * 2004-09-30 2007-08-31 삼성전자주식회사 Apparatus and method for object localization, tracking, and separation using audio and video sensors
DE102005044617A1 (en) 2004-10-01 2006-04-13 Vorwerk & Co. Interholding Gmbh A method for maintaining and / or cleaning a floor covering, floor, floor care and cleaning device or this
US7430462B2 (en) 2004-10-20 2008-09-30 Infinite Electronics Inc. Automatic charging station for autonomous mobile machine
US8078338B2 (en) 2004-10-22 2011-12-13 Irobot Corporation System and method for behavior based control of an autonomous vehicle
KR100656701B1 (en) 2004-10-27 2006-12-13 삼성광주전자 주식회사 Robot cleaner system and Method for return to external charge apparatus
JP4485320B2 (en) 2004-10-29 2010-06-23 アイシン精機株式会社 The fuel cell system
KR100575708B1 (en) 2004-11-11 2006-04-25 엘지전자 주식회사 Distance detection apparatus and method for robot cleaner
KR20060059006A (en) 2004-11-26 2006-06-01 삼성전자주식회사 Method and apparatus of self-propelled mobile unit with obstacle avoidance during wall-following
JP4277214B2 (en) 2004-11-30 2009-06-10 日立アプライアンス株式会社